A new feature selection algorithm for stream Data Classification

Feature selection, the process of removing irrelevant features can be extremely useful in reducing the dimensionality of the data to be processed, in reducing execution time and improving predictive accuracy of the classifier. Selecting the right set of features for classification is one of the most important problems in designing a good classifier. Feature selection methods can be decomposed into two broad classes. One is Filter methods and another is Wrapper method. This paper proposes hybrid method which is based on the Mutual Information and Genetic Algorithm.