Journal article

A weighted multiple classifier framework based on random projection

Thanh Nguyen Tien, Truong Dang Manh, Alan WeeChung Liew, James C Bezdek

Information Sciences | ELSEVIER SCIENCE INC | Published : 2019

Abstract

In this paper, we propose a weighted multiple classifier framework based on random projections. Similar to the mechanism of other homogeneous ensemble methods, the base classifiers in our approach are obtained by a learning algorithm on different training sets generated by projecting the original up-space training set to lower dimensional down-spaces. We then apply a Least SquarE−based method to weigh the outputs of the base classifiers so that the contribution of each classifier to the final combined prediction is different. We choose Decision Tree as the learning algorithm in the proposed framework and conduct experiments on a number of real and synthetic datasets. The experimental results..

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