Software Engineering

Weka - Data Mining Tool   Experiment  (ALL at once- “Adult, Irish, Zoo” & “J48, Naïve Bayes, KNN) In this experiment I am using all algorithms and datasets at once in order to show the comparison and do the evaluation among the datasets and its correctness including standard deviation.   Here I am comparing and evaluating the previous results. v – significantly better, *-significantly worse Evaluating J48, Naïve Bayes, KNN on Zoo dataset:  We got average of 92.66(SD: 7.07), 93.69(6.99) and 96.06(5.41) percent correct using J48, Naïve Bayes, KNN on zoo dataset. It’s a  10 fold cross validation so, if we want to see individual result we can save result on CVS file from setup panel. Evaluating J48, Naïve Bayes, KNN on Iris dataset:  We got average of 95.33, 92.60, 86.47  percent correct using J48, Naïve Bayes, KNN on iris dataset. It’s a  10 fold cross validation so, if we want to see individual result we can save result on CVS file from setup panel. Eva

Weka - Data Mining Tool  Algorithms: There are a lot of algorithm in weka for various classification and experiments and some the major and widely used are following : Decision tree(J48): NAME: weka.classifiers.trees.J48 SYNOPSIS: Class for generating a pruned or Un pruned  C4.5 decision tree. Naïve Bayes: NAME: weka.classifiers.bayes.NaiveBayes SYNOPSIS: Class for a Naive Bayes classifier using estimator classes. Numeric estimator precision values are chosen based on analysis of the training data. For this reason, the classifier is not an UpdateableClassifier (which in typical usage are initialized with zero training instances) KNN(IBK): NAME: weka.classifiers.lazy.IBk SYNOPSIS: K-nearest neighbours classifier. Can select appropriate value of K based on cross-validation. Can also do distance weighting. SVM(LibSVM):  NAME: weka.classifiers.functions.LibSVM SYNOPSIS: A wrapper class for the libsvm tools (the libsvm classes,