A High Diversit lybrid Ensemble of Classifiers
Ensemble has been proved a successful approach for enhancing the performance of single classifiers. But there are two key factors influencing the performance of an ensemble directly:accuracy of each single member and diversity between the members. There have been many approaches used in the literature to create the mentioned diversity. In this paper we add a novel approach, in which classifier type variance is utilized along with feature subset diversification to create a high diversity ensemble of different classifiers and an optimization is conducted on the initial population using a multi-objective evolutionary algorithm. The results of experiment over some standard datasets exhibit the outperformance of the suggested approach in comparison to existing ones in specific situations.
hybrid ensemble en.semble diversity genetic algorithm
Sahand Khakabimamaghani Farnaz Barzinpour Mohammad Reza Gholamian
Industrial Engineering Department Iran University of Science and Technology(IUST),Narmak. Tehran, Iran
国际会议
成都
英文
401-406
2010-06-23（万方平台首次上网日期，不代表论文的发表时间）