Based Data Mining Approach for Quality Control

sieveification-Based entropy Mining Approach For feeling harbour In Wine Production GUIDED BY SUBMITTED BY Jayshri Patel Hardik Barfiwala INDEX Sr No salmagundi of address Page No. 1 Introduction Wine Production 2 Objectives 3 Introduction To entropyset 4 Pre-Processing 5 Statistics Used In algorithmic programic programic rules 6 Algorithms Applied On Dataset 7 Comparison Of Applied Algorithm 8 Applying examination Dataset 9 Achievements 1. inst tot every last(predicate)yation TO WINE PRODUCTION * Wine diligence is modernly growing come up in the market since the make it decade. However, the prime(prenominal) factor in booze has become the important contend in vino making and selling. * To meet the increasing demand, assessing the bore of fuddle is necessary for the wine-colored application to prevent tampering of wine whole step as well as maintaining it. * To remain competitive, wine industry is investing in juvenile technologies like data mining for analyzing taste and opposite properties in wine. Data mining proficiencys provide more than spiritmary, just now worth(predicate)(predicate) entropy such as patterns and relationships mingled with wine properties and human taste, all of which earth-closet be apply to improve termination making and optimize chances of victor in both marketing and selling. * Two key elements in wine industry be wine certification and fictional character assessment, which atomic number 18 usually conducted via physicochemical and sensorial rises. * Physicochemical tests argon lab-based and are employ to characterize physicochemical properties in wine such as its density, alcohol or pH ranges. * pie-eyedwhile, sensory tests such as taste preference are performed by human experts.Taste is a contingent lieu that indicates quality in wine, the success of wine industry leave behind be greatly determined by consumer satisfaction in taste requirements. * Physicochemical data are ali ke piece useful in predicting human wine taste preference and classifying wine based on aroma chromatograms. 2. OBJECTIVE * casting the complex human taste is an important focus in wine industries. * The main purpose of this chew over was to predict wine quality based on physicochemical data. * This study was also conducted to identify bug outlier or anomaly in consume wine set in order to detect ruining of wine. 3. INTRODUCTION TO DATASETTo evaluate the performance of data mining dataset is taken into consideration. The array content describes the source of data. * Source Of Data Prior to the experimental part of the research, the data is gathered. It is gathered from the UCI Data Repository. The UCI Repository of Machine Learning Databases and ground Theories is a free Internet repository of analytical datasets from several areas. All datasets are in textbook files format provided with a short description. These datasets received recognition from many scientists and are cl aimed to be a valuable source of data. * Overview Of Dataset INFORMATION OF DATASETTitle Wine woodland Data engraft Characteristics Multivariate Number Of Instances WHITE-WINE 4898 RED-WINE 1599 theatre Business Attribute Characteristic Real Number Of Attribute 11 + Output Attribute wanting(p) Value N/A * Attribute Information * Input variable quantitys (based on physicochemical tests) * Fixed acidulousness Amount of Tartaric Acid present in wine. (In mg per liter) Used for taste, feel and color of wine. * fickle Acidity Amount of Acetic Acid present in wine. (In mg per liter) Its presence in wine is mainly due to yeast and bacterial metabolism. * Citric Acid Amount of Citric Acid present in wine. In mg per liter) Used to acidify wine that are too basic and as a flavor additive. * Residual Sugar The concentration of sugar remaining after fermentation. (In grams per liter) * Chlorides Level of Chlorides added in wine. (In mg per liter) Used to correct mineral deficiencies i n the brewing water. * issue Sulfur Dioxide Amount of Free Sulfur Dioxide present in wine. (In mg per liter) * Total Sulfur Dioxide Amount of free and combined south dioxide present in wine. (In mg per liter) Used mainly as preservative in wine process. * constriction The density of wine is close to that of water, dry wine is less and sweet wine is high(prenominal). In kg per liter) * PH Measures the quantity of acids present, the strength of the acids, and the effects of minerals and other ingredients in the wine. (In time value) * Sulphates Amount of sodium metabisulphite or kibibyte metabisulphite present in wine. (In mg per liter) * Alcohol Amount of Alcohol present in wine. (In percentage) * Output variable (based on sensory data) * feel (score between 0 and 10) White Wine 3 to 9 rubor Wine 3 to 8 4. PRE-PROCESSING * Pre-processing Of Data Preprocessing of the dataset is carried out before mining the data to remove the unalike lacks of the information in the data so urce.Following different process are carried out in the preprocessing reasons to make the dataset supple to perform miscellanea process. * Data in the real world is dirty because of the following reason. * Incomplete scatty specify values, lacking certain attributes of interest, or containing only aggregate data. * E. g. Occupation= * Noisy Containing wrongful conducts or outliers. * E. g. recompense=-10 * Inconsistent Containing discrepancies in codes or grade. * E. g. Age=42 Birthday=03/07/1997 * E. g. Was rating 1,2,3, Now rating A, B, C * E. g. Discrepancy between duplicate records * No quality data, no quality mining results Quality decisions essential be based on quality data. * Data warehouse needs consistent integration of quality data. * Major Tasks in d unrivaled in the Data Preprocessing are, * Data Cleaning * Fill in deficient values, smooth noisy data, identify or remove outliers, and resolve inconsistencies. * Data integration * Integration of threefold da tabases, data cubes, or files. * The dataset provided from given data source is only in one single file. So there is no need for integrating the dataset. * Data transformation * Normalization and aggregation * The dataset is in Normalized form because it is in single data file. * Data reduction Obtains reduced representation in volume but produces the same or similar analytical results. * The data volume in the given dataset is not very huge, the procedure of performing different algorithm is easily done on dataset so the reduction of dataset is not needed on the data set * Data discretization * Part of data reduction but with particular importance, especially for numericalal data. * Need for Data Preprocessing in wine quality, * For this dataset Data Cleaning is only requisite in data pre-processing. * Here, NumericTonominal, InterquartileRange and RemoveWithValues penetrates are used for data pre-processing. * NumericToNominal deform weka. sieves. unsupervised. attribute. Nu mericToNominal) * A filter for turning numeric attribute into nominal once. * In our dataset, Class attribute Quality in both dataset (Red-wine Quality, White-wine Quality) prevail a type Numeric. So after applying this filter, class attribute Quality convert into type Nominal. * And Red-wine Quality dataset remove class names 3, 4, 5 8 and White-wine Quality dataset have class names 3, 4, 5 9. * Because of classification does not apply on numeric type class field, there is a need for this filter. * InterquartileRange Filter (weka. filters. unsupervised. attribute. InterquartileRange) A filter for detecting outliers and fundamental values based on interquartile commences. The filter skips the class attribute. * Apply this filter for all attribute indices with all default options. * After applying, filter adds cardinal more fields which names are Outliers and ExtremeValue. And this fields has two types of label No and Yes. Here Yes label indicates, there are outliers and extre me values in dataset. * In our dataset, there are 83 extreme values and one hundred twenty-five outliers in White-wine Quality dataset and 69 extreme values and 94 outliers in Red-wine Quality. * RemoveWithValues Filter (weka. filters. unsupervised. instance.RemoveWithValues) * Filters instances according to the value of an attribute. * This filter has two options which are AttributeIndex and NominalIndices. * AttributeIndex choose attribute to be use for alternative and NominalIndices choose range of label indices to be use for filling on nominal attribute. * In our dataset, AttributeIndex is last and NominalIndex is also last, so It will remove first 83 extreme values and whence 125 outliers in White-wine Quality dataset and 69 extreme values and 94 outliers in Red-wine Quality. * After applying this filter on dataset remove both fields from dataset. * Attribute SelectionRanking Attributes Using Attribute Selection Algorithm RED-WINE graded WHITE-WINE Volatile_Acidity(2) 0. 1 248 0. 0406 Volatile_Acidity(2) Total_sulfer_Dioxide(7) 0. 0695 0. 0600 Citric_Acidity(3) Sulphates(10) 0. 1464 0. 0740 Chlorides(5) Alcohal(11) 0. 2395 0. 0462 Free_Sulfer_Dioxide(6) 0. 1146 Density(8) 0. 2081 Alcohal(11) * The recogniseion of attributes is performed automatically by WEKA use Info Gain Attribute Eval method. * The method evaluates the worth of an attribute by measuring the information gain with respect to the class. 5. STATISTICS USED IN algorithmS * Statistics MeasuresThere are incompatible algorithms that whoremonger be used while performing data mining on the different dataset using weka, some of them are describe below with the different statistics circulars. * Statistics Used In Algorithms * Kappa statistic * The kappa statistic, also called the kappa co high-octane, is a performance criterion or index which canvass the agreement from the lesson with that which could occur merely by chance. * Kappa is a measure of agreement normalized for chance ag reement. * Kappa statistic describe that our prediction for class attribute for given dataset is how much near to effective values. * Values Range For Kappa Range settlement lt0 POOR 0-0. 20 SLIGHT 0. 21-0. 40 plum 0. 41-0. 60 MODERATE 0. 61-0. 80 SUBSTANTIAL 0. 81-1. 0 ALMOST PERFECT * As above range in weka algorithm evaluation if value of kappa is near to 1 then our predicted values are accurate to actual values so, applied algorithm is accurate. Kappa Statistic Values For Wine Quality DataSet Algorithm White-wine Quality Red-wine Quality K-Star 0. 5365 0. 5294 J48 0. 3813 0. 3881 Multilayer Perceptron 0. 2946 0. 3784 * humble authoritative misunderstanding (MAE) * Mean imperative misplay (MAE)is a quantity used to measure how close forecasts or predictions are to the eventual essences. The mean absolute error is given by, Mean absolute geological fault For Wine Quality DataSet Algorithm White-wine Quality Red-wine Quality K-Star 0. 1297 0. 1381 J48 0. 1245 0. 1401 Mult ilayer Perceptron 0. 1581 0. 1576 * extraction Mean square up defect * If you have some data and try to make a curve (a formula) fit them, you butt graph and see how close the curve is to the capitulums. Another measure of how well the curve fits the data is get-go Mean square up misconduct. * For each data point, CalGraph calculates the value ofy from the formula. It subtracts this from the datas y-value and squares the difference. All these squares are added up and the sum is divided by the number of data. * Finally CalGraph takes the square root. Written mathematically, start Mean Square break is Root Mean Squared erroneous belief For Wine Quality DataSet Algorithm White-wine Quality Red-wine Quality K-Star 0. 2428 0. 2592 J48 0. 3194 0. 3354 Multilayer Perceptron 0. 2887 0. 3023 * Root copulation Squared Error * Theroot sexual congress shape erroris sexual intercourse to what it would have been if a ingenuous predictor had been used. More specifically, this simpl e predictor is just the average of the actual values. Thus, the relative square error takes the nub squared error and normalizes it by dividing by the total squared error of the simple predictor. * By taking the square root of therelative squared errorone reduces the error to the same dimensions as the quantity being predicted. * Mathematically, theroot relative squared errorEiof an individual programiis evaluated by the equation * whereP(ij)is the value predicted by the individual programifor sample casej(out ofnsample cases)Tjis the target value for sample casej andis given by the formula * For a perfect fit, the numerator is equal to 0 andEi= 0.So, theEiindex ranges from 0 to infinity, with 0 ticking to the ideal. Root recounting Squared Error For Wine Quality DataSet Algorithm White-wine Quality Red-wine Quality K-Star 78. 1984 % 79. 309 % J48 102. 9013 % 102. 602 % Multilayer Perceptron 93. 0018 % 92. 4895 % * coitus unattackable Error * Therelative absolute erroris very s imilar to therelative squared errorin the sense that it is also relative to a simple predictor, which is just the average of the actual values. In this case, though, the error is just the total absolute error instead of the total squared error. Thus, the relative absolute error takes the total absolute error and normalizes it by dividing by the total absolute error of the simple predictor. Mathematically, therelative absolute errorEiof an individual programiis evaluated by the equation * whereP(ij)is the value predicted by the individual programifor sample casej(out ofnsample cases)Tjis the target value for sample casej andis given by the formula * For a perfect fit, the numerator is equal to 0 andEi= 0. So, theEiindex ranges from 0 to infinity, with 0 corresponding to the ideal.Relative infinite Squared Error For Wine Quality DataSet Algorithm White-wine Quality Red-wine Quality K-Star 67. 2423 % 64. 5286 % J48 64. 577 % 65. 4857 % Multilayer Perceptron 81. 9951 % 73. 6593 % * di fferent swans * There are four achievable outcomes from a classifier. * If the outcome from a prediction ispand the actual value is alsop, then it is called atrue controlling(TP). * However if the actual value isnthen it is said to be afalse positive(FP). * Conversely, atrue invalidating(TN) has occurred when both the prediction outcome and the actual value aren. Andfalse negatively charged(FN) is when the prediction outcome isn while the actual value isp. * secure Value P N TOTAL p True positive false positive P n false negative True negative N Total P N * ROC Curves * While estimating the effectiveness and accuracy of data mining technique it is essential to measure the error rate of each method. * In the case of binary classification tasks the error rate takes and components under consideration. * The ROC analysis which stands for Receiver Operating Characteristics is applied. * The sample ROC curve is presented in the Figure below.The immediate the ROC curve is to the t op left corner of the ROC graph the better the performance of the classifier. * Sample ROC curve (squares with the economic consumption of the model, triangles without). The line connecting the square with triage is the benefit from the usage of the model. * It plots the curve which consists of x-axis presenting false positive rate and y-axis which plots the true positive rate. This curve model selects the optimal model on the basis of assumed class distribution. * The ROC curves are relevant e. g. in decision maneuver models or rule sets. * disavow, precision and F-Measure There are four possible results of classification. * Different conspiracy of these four error and correct situations are presented in the scientific literature on topic. * Here three popular notions are presented. The introduction of these classifiers is explained by the possibility of high accuracy by negative type of data. * To avoid such situation recall and precision of the classification are introduced . * The F measure is the harmonic mean of precision and recall. * The formal definitions of these measures are as follow PRECSION = TPTP+FP RECALL = TPTP+FNF-Measure = 21PRECSION+1RECALL * These measures are introduced especially in information retrieval application. * muddiness intercellular substance * A matrix used to summarise the results of a supervised classification. * Entries along the main diagonal are correct classifications. * Entries other than those on the main diagonal are classification errors. 6. ALGORITHMS * K-nearest Neighbor Classifiers * Nearest neighbor classifiers are based on acquisition by analogy. * The pedagogy samples are described by n-dimensional numeric attributes. Each sample represents a point in an n-dimensional space. In this way, all of the training samples are stored in an n-dimensional pattern space. When given an unknown sample, a k-nearest neighbor classifier searches the pattern space for the k training samples that are closest to the unk nown sample. * These k training samples are the k-nearest neighbors of the unknown sample. Closeness is defined in terms of Euclidean distance, where the Euclidean distance between two points, , * The unknown sample is allegeed the most common class among its k nearest neighbors. When k = 1, the unknown sample is assigned the class of the training sample that is closest to it in pattern space. Nearest neighbor classifiers are instance-based or lazy learners in that they store all of the training samples and do not reach a classifier until a new (unlabeled) sample needs to be classified. * Lazy learners washbasin amaze expensive computational costs when the number of potential neighbors (i. e. , stored training samples) with which to compare a given unlabeled sample is great. * Therefore, they require efficient indexing techniques. As expected, lazy learning methods are faster at training than eager methods, but slower at classification since all computation is delayed to that ti me.Unlike decision tree induction and back propagation, nearest neighbor classifiers assign equal weight to each attribute. This clean-livingthorn cause confusion when there are many irrelevant attributes in the data. * Nearest neighbor classifiers can also be used for prediction, i. e. to return a real-valued prediction for a given unknown sample. In this case, the classifier returns the average value of the real-valued labels associated with the k nearest neighbors of the unknown sample. * In weka the previously described algorithm nearest neighbor is given as Kstar algorithm in classifier - lazy tab. The Result Generated After Applying K-Star On White-wine Quality Dataset Kstar Options -B 70 -M a Time Taken To chassis Model 0. 02 Seconds Stratified Cross- organisation (10-Fold) * Summary justly class Instances 3307 70. 6624 % incorrectly classified advertisement Instances 1373 29. 3376 % Kappa Statistic 0. 5365 Mean Absolute Error 0. 1297 Root Mean Squared Error 0. 2428 Relative Absolute Error 67. 2423 % Root Relative Squared Error 78. 1984 % Total Number Of Instances 4680 * small Accuracy By Class TP pace FP Rate Precision Recall F-Measure ROC domain PRC Area Class 0 0 0 0 0 0. 583 0. 004 3 0. 211 0. 002 0. 769 0. 211 0. 331 0. 884 0. 405 4 0. 672 0. 079 0. 777 0. 672 0. 721 0. 904 0. 826 5 0. 864 0. 378 0. 652 0. 864 0. 743 0. 84 0. 818 6 0. 536 0. 031 0. 797 0. 536 0. 641 0. 911 0. 772 7 0. 398 0. 002 0. 883 0. 398 0. 548 0. 913 0. 572 8 0 0 0 0 0 0. 84 0. 014 9 Weighted Avg. 0. 707 0. 2 0. 725 0. 707 0. 695 0. 876 0. 787 * Confusion Matrix A B C D E F G Class 0 0 4 9 0 0 0 A=3 0 30 49 62 1 0 0 B=4 0 7 919 437 5 0 0 C=5 0 2 201 1822 81 2 0 D=6 0 0 9 389 468 7 0 E=7 0 0 0 73 30 68 0 F=8 0 0 0 3 2 0 0 G=9 * feat Of The Kstar With Respect To A Testing manikin For The White-wine Quality DatasetTesting rule T raining Set Testing Set 10-Fold Cross Validation 66% fragmented Correctly Classified Instances 99. 6581 % 100 % 70. 6624 % 63. 9221 % Kappa statistic 0. 9949 1 0. 5365 0. 4252 Mean Absolute Error 0. 0575 0. 0788 0. 1297 0. 1379 Root Mean Squared Error 0. 1089 0. 145 0. 2428 0. 2568 Relative Absolute Error 29. 8022 % 67. 2423 % 71. 2445 % * The Result Generated After Applying K-Star On Red-wine Quality Dataset Kstar Options -B 70 -M a Time Taken To Build Model 0 Seconds Stratified Cross-Validation (10-Fold) * Summary Correctly Classified Instances 1013 71. 379 % Incorrectly Classified Instances 413 28. 9621 % Kappa Statistic 0. 5294 Mean Absolute Error 0. 1381 Root Mean Squared Error 0. 2592 Relative Absolute Error 64. 5286 % Root Relative Squared Error 79. 309 % Total Number Of Instances 1426 * Detailed Accuracy By Class TP Rate FP Rate Precision Recall F-Measure ROC Area PRC Area Class 0 0. 001 0 0 0 0. 574 0. 019 3 0 0. 003 0 0 0 0. 811 0. 114 4 0. 791 0. 176 0. 67 0. 791 0. 779 0. 894 0. 867 5 0. 769 0. 26 0. 668 0. 769 0. 715 0. 834 0. 788 6 0. 511 0. 032 0. 692 0. 511 0. 588 0. 936 0. 722 7 0. 125 0. 001 0. 5 0. 125 0. 2 0. 896 0. 142 8 Weighted Avg. 0. 71 0. 184 0. 685 0. 71 0. 693 0. 871 0. 78 * Confusion Matrix A B C D E F Class 0 1 4 1 0 0 A=3 1 0 30 17 0 0 B=4 0 2 477 120 4 0 C=5 0 1 103 444 29 0 D=6 0 0 8 76 90 2 E=7 0 0 0 7 7 2 F=8 Performance Of The Kstar With Respect To A Testing Configuration For The Red-wine Quality Dataset Testing method acting Training Set Testing Set 10-Fold Cross Validation 66% discontinue Correctly Classified Instances 99. 7895 % 100 % 71. 0379 % 70. 7216 % Kappa statistic 0. 9967 1 0. 5294 0. 5154 Mean Absolute Error 0. 0338 0. 0436 0. 1381 0. 1439 Root Mean Squared Error 0. 0675 0. 0828 0. 2592 0. 2646 Relative Absolute Error 15. 8067 % 64. 5286 % 67. 4903 % * J48 Decision Tree * Class for generating a pruned or unpruned C4. 5 decision tree. A decision tree is a predictive machine-learning model that decides the target value (dependent variable) of a new sample based on various attribute values of the available data. * The internal nodes of a decision tree denote the different attribute the branches between the nodes secern us the possible values that these attributes can have in the observed samples, while the terminal nodes tell us the final value (classification) of the dependent variable. * The attribute that is to be predicted is known as the dependent variable, since its value depends upon, or is decided by, the values of all the other attributes.The other attributes, which help in predicting the value of the dependent variable, are known as the independent variables in the dataset. * The J48 Decision tree classifier follows the following simple algorithm * In order to classify a new item, it first needs to create a decision tree based on the attribute values of the avai lable training data. So, whenever it encounters a set of items (training set) it identifies the attribute that discriminates the various instances most clearly. * This feature that is able to tell us most about the data instances so that we can classify them the better(p) is said to have the highest information gain. Now, among the possible values of this feature, if there is any value for which there is no ambiguity, that is, for which the data instances falling within its category have the same value for the target variable, then we terminate that branch and assign to it the target value that we have obtained. * For the other cases, we then look for another attribute that gives us the highest information gain. Hence we continue in this manner until we either get a clear decision of what combination of attributes gives us a particular target value, or we run out of attributes.In the event that we run out of attributes, or if we cannot get an unambiguous result from the available information, we assign this branch a target value that the majority of the items under this branch possess. * Now that we have the decision tree, we follow the order of attribute selection as we have obtained for the tree. By checking all the respective attributes and their values with those seen in the decision tree model, we can assign or predict the target value of this new instance. * The Result Generated After Applying J48 On White-wine Quality Dataset Time Taken To Build Model 1. 4 Seconds Stratified Cross-Validation (10-Fold) * Summary Correctly Classified Instances 2740 58. 547 % Incorrectly Classified Instances 1940 41. 453 % Kappa Statistic 0. 3813 Mean Absolute Error 0. 1245 Root Mean Squared Error 0. 3194 Relative Absolute Error 64. 5770 % Root Relative Squared Error 102. 9013 % Total Number Of Instances 4680 * Detailed Accuracy By Class TP Rate FP Rate Precision Recall F-Measure ROC Area Class 0 0. 002 0 0 0 0. 30 3 0. 239 0. 020 0. 270 0. 239 0. 254 0. 699 4 0. 605 0. 169 0. 597 0. 605 0. 601 0. 763 5 0. 644 0. 312 0. 628 0. 644 0. 636 0. 689 6 0. 526 0. 099 0. 549 0. 526 0. 537 0. 766 7 0. 363 0. 022 0. 388 0. 363 0. 375 0. 75 8 0 0 0 0 0 0. 496 9 Weighted Avg. 0. 585 0. 21 0. 582 0. 585 0. 584 0. 727 * Confusion Matrix A B C D E F G Class 0 2 6 5 0 0 0 A=3 1 34 55 44 6 2 0 B=4 5 50 828 418 60 7 0 C=5 2 32 413 1357 261 43 0 D=6 7 76 286 459 44 0 E=7 1 1 10 49 48 62 0 F=8 0 0 0 1 2 2 0 G=9 * Performance Of The J48 With Respect To A Testing Configuration For The White-wine Quality Dataset Testing Method Training Set Testing Set 10-Fold Cross Validation 66% Split Correctly Classified Instances 90. 1923 % 70 % 58. 547 % 54. 8083 % Kappa statistic 0. 854 0. 6296 0. 3813 0. 33 Mean Absolute Error 0. 0426 0. 0961 0. 1245 0. 1347 Root Mean Squared Error 0. 1429 0. 2756 0. 3194 0. 3397 Relative Absolute Error 22. 0695 % 64. 577 % 69. 84 % * The Result Generated After Applying J48 On Red-wine Quality Dataset Time Taken To Build Model 0. 17 Seconds Stratified Cross-Validation * Summary Correctly Classified Instances 867 60. 7994 % Incorrectly Classified Instances 559 39. 2006 % Kappa Statistic 0. 3881 Mean Absolute Error 0. 1401 Root Mean Squared Error 0. 3354 Relative Absolute Error 65. 4857 % Root Relative Squared Error 102. 602 % Total Number Of Instances 1426 * Detailed Accuracy By Class Tp Rate Fp Rate Precision Recall F-measure Roc Area Class 0 0. 004 0 0 0 0. 573 3 0. 063 0. 037 0. 056 0. 063 0. 059 0. 578 4 0. 721 0. 258 0. 672 0. 721 0. 696 0. 749 5 0. 57 0. 238 0. 62 0. 57 0. 594 0. 674 6 0. 563 0. 64 0. 553 0. 563 0. 558 0. 8 7 0. 063 0. 006 0. 1 0. 063 0. 077 0. 691 8 Weighted Avg. 0. 608 0. 214 0. 606 0. 608 0. 606 0. 718 * Confusion Matrix A B C D E F Class 0 2 1 2 1 0 A=3 2 3 25 15 3 0 B=4 1 26 435 122 17 2 C=5 2 21 167 329 53 5 D=6 0 2 16 57 99 2 E=7 0 0 3 6 6 1 F=8 Performance Of The J48 With Respect To A Testing Configuration For The Red-wine Quality Dataset Testing Method Training Set Testing Set 10-Fold Cross Validation 66% Split Correctly Classified Instances 91. 1641 % 80 % 60. 7994 % 62. 4742 % Kappa statistic 0. 8616 0. 6875 0. 3881 0. 3994 Mean Absolute Error 0. 0461 0. 0942 0. 1401 0. 1323 Root Mean Squared Error 0. 1518 0. 2618 0. 3354 0. 3262 Relative Absolute Error 21. 5362 % 39. 3598 % 65. 4857 % 62. 052 % * Multilayer Perceptron * The back propagation algorithm performs learning on a multilayer feed-forward neural network. It iteratively learns a set of weights for prediction of the class label of tuples. * A multilayer feed-forward neural network consists of an comment layer, one or more hidden layers, and an sidetrack layer. * Each layer is made up of units. The inputs to the network correspond to the attributes measured for each training tuple. The inputs are fed simultaneously into the units making up the input layer. These inputs distribute through the input layer and are then heavy and fed simultaneously to a twinkling layer of neuronlike units, known as a hidden layer. The outturns of the hidden layer units can be input to another hidden layer, and so on. The number of hidden layers is arbitrary, although in practice, usually only one is used. The weighted outputs of the last hidden layer are input to units making up the output layer, which emits the networks prediction for given tuples. * The units in the input layer are called input units. The units in the hidden layers and output layer are sometimes referred to as neurodes, due to their symbolic biological basis, or as output units. * The network is feed-forward in that none of the weights cycles back to an input unit or to an output unit of a previous layer.It is fully connected in that each unit provides input to each unit in the next forward layer. * The Result Generated After Applying Multilayer Perceptron On White-win e Quality Dataset Time taken to build model 36. 22 seconds Stratified cross- brass * Summary Correctly Classified Instances 2598 55. 5128 % Incorrectly Classified Instances 2082 44. 4872 % Kappa statistic 0. 2946 Mean absolute error 0. 1581 Root mean squared error 0. 2887 Relative absolute error 81. 9951 % Root relative squared error 93. 0018 % Total Number of Instances 4680 * Detailed Accuracy By Class TP Rate FP Rate Precision Recall F-Measure ROC Area PRC Area Class 0 0 0 0 0 0. 344 0. 002 3 0. 056 0. 004 0. 308 0. 056 0. 095 0. 732 0. 156 4 0. 594 0. 165 0. 597 0. 594 0. 595 0. 98 0. 584 5 0. 704 0. 482 0. 545 0. 704 0. 614 0. 647 0. 568 6 0. 326 0. 07 0. 517 0. 326 0. 4 0. 808 0. 474 7 0. 058 0. 002 0. 5 0. 058 0. cv 0. 8 0. 169 8 0 0 0 0 0 0. 356 0. 001 9 Weighted Avg. 0. 555 0. 279 0. 544 0. 555 0. 532 0. 728 0. 526 * Confusion Matrix A B C D E F G Class 0 0 5 7 1 0 0 A=3 0 8 82 50 2 0 0 B=4 0 11 812 532 12 1 0 C=5 0 6 425 1483 188 6 0 D=6 0 1 33 551 285 3 0 E=7 0 0 3 98 60 10 0 F=8 0 0 0 2 3 0 0 G=9 * Performance Of The Multilayer perceptron With Respect To A Testing Configuration For The White-wine Quality DatasetTesting Method Training Set Testing Set 10-Fold Cross Validation 66% Split Correctly Classified Instances 58. 1838 % 50 % 55. 5128 % 51. 3514 % Kappa statistic 0. 3701 0. 3671 0. 2946 0. 2454 Mean Absolute Error 0. 1529 0. 1746 0. 1581 0. 1628 Root Mean Squared Error 0. 2808 0. 3256 0. 2887 02972 Relative Absolute Error 79. 2713 % 81. 9951 % 84. 1402 % * The Result Generated After Applying Multilayer Perceptron On Red-wine Quality Dataset Time taken to build model 9. 14 seconds Stratified cross-validation (10-Fold) * Summary Correctly Classified Instances 880 61. 111 % Incorrectly Classified Instances 546 38. 2889 % Kappa statistic 0. 3784 Mean absolute error 0. 1576 Root mean squared error 0. 3023 Relative absolute error 73. 6593 % Root relative squared error 92. 4895 % Total Number of Instances 1426 * Detailed Accuracy By Class TP Rate FP Rate Precision Recall F-Measure ROC Area Class 0 0 0 0 0 0. 47 3 0. 42 0. 005 0. 222 0. 042 0. 070 0. 735 4 0. 723 0. 249 0. 680 0. 723 0. 701 0. 801 5 0. 640 0. 322 0. 575 0. 640 0. 605 0. 692 6 0. 415 0. 049 0. 545 0. 415 0. 471 0. 831 7 0 0 0 0 0 0. 853 8 Weighted Avg. 0. 617 0. 242 0. 595 0. 617 0. 602 0. 758 * Confusion Matrix A B C D E F Class 0 5 1 0 0 A=3 0 2 34 11 1 0 B=4 0 2 436 one hundred sixty 5 0 C=5 0 5 156 369 47 0 D=6 0 0 10 93 73 0 E=7 0 0 0 8 8 0 F=8 * Performance Of The Multilayer perceptron With Respect To A Testing Configuration For The Red-wine Quality Dataset Testing Method Training Set Testing Set 10-Fold Cross Validation 66% Split Correctly Classified Instances 68. 7237 % 70 % 61. 7111 % 58. 7629 % Kappa statistic 0. 4895 0. 5588 0. 3784 0. 327 Mean Absolute Error 0. 426 0. 1232 0. 1576 0. 1647 Root Mean Squared Error 0. 2715 0. 2424 0. 3023 0. 3029 Relative Absolute Error 66. 6774 % 51. 4904 % 73. 6593 % 77. 2484 % * Result * The classification experiment is measured by accuracy percentage of classifying the instances correctly into its class according to quality attributes ranges between 0 (very bad) and 10 (excellent). * From the experiments, we found that classification for red wine quality usingKstar algorithm achieved 71. 0379 % accuracy while J48 classifier achieved about 60. 7994% and Multilayer Perceptron classifier achieved 61. 7111% accuracy. For the egg white wine, Kstar algorithm yielded 70. 6624 % accuracy while J48 classifier yielded 58. 547% accuracy and Multilayer Perceptron classifier achieved 55. 5128 % accuracy. * Results from the experiments lead us to conclude that Kstar performs better in classification task as compared against the J48 and Multilayer Perceptron classifier. The processing time for Kstar algorithm is also observed to be more efficient and less time consuming despite the large size of wine properties dataset. 7. COMPARISON OF DIFFERENT ALGORITHM * The Comparison Of All Three Algorithm On White-wine Quality Dataset (Using 10-Fold Cross Validation) Kstar J48 Multilayer Perceptron Time (Sec) 0 1. 08 35. 14 Kappa Statistics 0. 5365 0. 3813 0. 29 Correctly Classified Instances (%) 70. 6624 58. 547 55. 128 True compulsive Rate (Avg) 0. 707 0. 585 0. 555 False coercive Rate (Avg) 0. 2 0. 21 0. 279 * Chart Shows The Best Suited Algorithm For Our Dataset (Measures Vs Algorithms) * In above chart, comparison of True Positive rate and kappa statistics is given against three algorithm Kstar, J48, Multilayer Perceptron * Chart describes algorithm which is best suits for our dataset. In above chart column of TP rate & Kappa statistics of Kstar algorithm is higher than other two algorithms. * In above cha rt you can see that the False Positive Rate and the Mean Absolute Error of the Multilayer Perceptron algorithm is high compare to other two algorithms. So it is not good for our dataset. * But for the Kstar algorithm these two values are less, so the algorithm having lowest values for FP Rate & Mean Absolute Error rate is best suited algorithm. * So the final we can make conclusion that the Kstar algorithm is best suited algorithm for White-wine Quality dataset. The Comparison Of All Three Algorithm On Red-wine Quality Dataset (Using 10-Fold Cross Validation) Kstar J48 Multilayer Perceptron Time (Sec) 0 0. 24 9. 3 Kappa Statistics 0. 5294 0. 3881 0. 3784 Correctly Classified Instances (%) 71. 0379 60. 6994 61. 7111 True Positive Rate (Avg) 0. 71 0. 608 0. 617 False Positive Rate (Avg) 0. 184 0. 214 0. 242 * For Red-wine Quality dataset have also Kstar is best suited algorithm , because of TP rate & Kappa statistics of Kstar algorithm is higher than other two algorithms and FP rate & Mean Absolute Error of Kstar algorithm is lower than other algorithms. . APPLYING exam DATASET Step1 Load pre-processed dataset. Step2 Go to classify tab. Click on choose button and select lazy brochure from the hierarchy tab and then select kstar algorithm. After selecting the kstar algorithm keep the value of cross validation = 10, then build the model by clicking on start button. Step3 Now take any 10 or 15 records from your dataset, make their class value unknown(by putting ? in the cell of the corresponding crude ) as shown below. Step 4 Save this data set as . rff file. Step 5 From test option display board select supplied test set, click on to the set button and capable the test dataset file which was lastly created by you from the disk. Step 6 From Result list panel panel select Kstar-algorithm (because it is better than any other for this dataset), right click it and click Re-evaluate model on current test set Step 7 Again right click on Kstar algorithm and select v isualize classifier error Step 8Click on fork up button and then save your test model.Step 9 After you had saved your test model, a separate file is created in which you will be having your predicted values for your testing dataset. Step 10 Now, this test model will have all the class value generated by model by re-evaluating model on the test data for all the instances that were set to unknown, as shown in the figure below. 9. ACHIEVEMENT * Classification models may be used as part of decision support system in different stages of wine production, hence giving the opportunity for manufacturer to make corrective and additive measure that will result in higher quality wine being produced. From the resulting classification accuracy, we found that accuracy rate for the white wine is influenced by a higher number of physicochemistry attribute, which are alcohol, density, free sulfur dioxide, chlorides, citric acid, and volatile acidity. * Red wine quality is highly correlated to only four attributes, which are alcohol, sulphates, total sulfur dioxide, and volatile acidity. * This shows white wine quality is affected by physicochemistry attributes that does not affect the red wine in general. Therefore, I hint that white wine manufacturer should conduct wider range of test particularly towards density and chloride content since white wine quality is affected by such substances. * Attribute selection algorithm we conducted also class-conscious alcohol as the highest in both datasets, hence the alcohol level is the main attribute that determines the quality in both red and white wine. * My suggestion is that wine manufacturer to focus in maintaining a suitable alcohol content, may be by longer fermentation period or higher yield fermenting yeast.