Data mining methods are used to handle the problems of dynamic huge data set. To build a classification model, time complexity of calculated result can be scale back by selecting only useful features. A feature selection technique is used to select only useful features from available features. An intersection principle based feature selection approach is Used. Genetic algorithm is used as a search method and it select only the features which are appears frequently in datasets. Then results were tested for different datasets having different type of data using Naive Bayes & J48 classifiers. The result analysis shows that Naive Bayes classifier gives better result than J48 classifier, with the substantial growth in accuracy, minimum time and minimum number of features. In this paper correlation feature selection is used with Genetic Algorithm for feature selection.

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Dimensionality Reduction Feature Selection Genetic Algorithm (ga) Naïve Bayes J48