Formulation of pdi-Bagging and its Evaluation

Abstract

For pattern classification problems, there is ensemble learning method that identifies multiple weak classifiers by the learning data and combines them together to improve the discriminant rate of testing data. We have already proposed pdi-Bagging (possibilistic data interpolation-Bagging) which improves the discriminant rate of testing data by adding virtually generated data to learning data. However, the accuracy of the correct virtual data type is not stable because the virtual data generate in the wide area of the data space. In addition, the discriminant accuracy is not high because the evaluation index for changing the generation class of virtual data is defined in each dimension. In this paper, we propose a new method to specify the generation area of virtual data and change the generation class of virtual data. As a result, the discriminant accuracy is improved since five new bagging methods which generate virtual data around correct discriminant data and error discriminant data are formulated, and the class of virtual data is determined with the proposed new evaluation index in multidimensional space. We formulate a new pdi-Bagging algorithm, and discuss the usefulness of the proposed method using numerical examples.