An Effective Algorithm for Correlation Attribute Subset Selection by Using Genetic Algorithm Based On Naive Bays Classifier

Abstract

In recent years, application of feature selection methods in various datasets has greatly increased. Feature selection is an important topic in data mining, especially for high dimensional datasets. Feature selection (also known as subset selection) is a process commonly used in machine learning, wherein subsets of the features available from the data are selected for application of a learning algorithm. The main idea of feature selection is to choose a subset of input variables by eliminating features with little or no predictive information. The challenging task in feature selection is how to obtain an optimal subset of relevant and non redundant features which will give an optimal solution without increasing the complexity of the modeling task. Feature selection that selects a subset of most salient features and removes irrelevant, redundant and noisy features is a process commonly employed in machine learning to solve the high dimensionality problem. It focuses learning algorithms on most useful aspects of data, thereby making learning task faster and more accurate. A data warehouse is designed to consolidate and maintain all features that are relevant for the analysis processes