Enhancing Classifier Performance Via Hybrid Feature Selection and Numeric Class Handling-A Comparative Study
Classification is a supervised machine learning procedure in which the effective model is constructed for prediction. The accuracy of classification mainly depends on the type of features and the characteristics of the dataset. Feature selection is an efficient approach in searching the most descriptive features which would contribute to the increase in the performance of the inductive algorithm by reducing dimensionality and processing time. In this paper a hybrid embedded feature selection algorithm with class label refining and handled numeric class problem in classifier are implemented. A novel feature selection algorithm based on ranker search optimization method and ensemble genetic search for selecting the appropriate features and class label refining for correcting misclassified instances from the dataset have been done.