Abstract
Data classification method is one of the main tasks of data mining. In the literature, there are many classic base inducers used to train the classifier such as decision tree, neural network…etc., which are all individual classifier. In the past few years, many researches have proposed that the classifier ensemble, which composed by more than one individual classifier, is more effective than any individual classifier of the classifier ensemble. The main idea for classifier ensemble to classify a new sample is to combine the output of each individual classifier and then reach the final decision. Therefore, the diversity between the classifiers is considered as an important factor in classification accuracy. Because there are few literatures to research about how to optimize the diversity, this paper would propose an ensemble method（Diversity by evolutionary computing resampling training subset, DECRTS）that uses the genetic algorithm to encourage the diversity between classifiers by manipulating the train data set. We design an experiment using 21 UCI Repository of machine learning databases to test and verify and then comparing with individual classifier and other classifier ensembles. The result provides that the DECRTS in our experiment has better average accuracy（82.19%）and is significantly difference with other method except Adaboost（81.99%）. Moreover, the experiment appears the different method to create diversity sometimes would have better performance in particular datasets.