Semantic Explanations in Ensemble Learning
A combination method is an integral part of an ensemble classifier.Existing combination methods determine the combined prediction of a new instance by relying on the predictions made by the majority of base classifiers.This can result in incorrect combined predictions when the majority predict the incorrect class.It has been noted that in group decision-making,the decision by the majority,if lacking consistency in the reasons for the decision provided by its members,could be less reliable than the minoritys decision with higher consistency in the reasons of its members.Based on this observation,in this paper,we propose a new combination method,EBCM,which considers the consistency of the features,i.e.explanations of individual predictions for generating ensemble classifiers.EBCM firstly identifies the features accountable for each base classifiers prediction,and then uses the features to measure the consistency among the predictions.Finally,EBCM combines the predictions based on both the majority and the consistency of features.We evaluated the performance of EBCM with 16 real-world datasets and observed substantial improvement over existing techniques.
Md.Zahidul Islam Jixue Liu Lin Liu Jiuyong Li Wei Kang
School of Information Technology and Mathematical Sciences(ITMS),University of South Australia,Adelaide,SA 5095,Australia
国际会议
澳门
英文
29-41
2019-04-14（万方平台首次上网日期，不代表论文的发表时间）