An Approach of Multiple Classifiers Ensemble Based on Feature Selection

In order to improve the classification performance of classifiers, an approach of multiple classifiers ensemble based on feature selection (FSCE) is proposed in the paper. After attributes of the training data set are specially selected, the new data set is mapped into new training data sets. There is the number of attributes (the class attribute excepted) of the new data sets. Then classifiers with better performance are selected from the classifiers that are trained in every small training data set. They are used to classify the corresponding small testing data sets that are disposed by attribute selection. FSCE is tested on the UCI benchmark data sets, and compared classification efficiency with member classifiers trained based on the algorithm of Adaboost. In this way, the utility of FSCE can be proved in the paper.