期刊名称：ELCVIA: electronic letters on computer vision and image analysis
印刷版ISSN：1577-5097
出版年度：2017
卷号：15
期号：3
页码：32-47
DOI：10.5565/rev/elcvia.929
语种：English
出版社：Centre de Visió per Computador
摘要：Some important topics, which affects directly on the performance of the designed ensemble classifier, inflict a complex search space with high dimensions on the researcher. So, heuristic algorithms can be applied to find best solutions because of their capability of efficient search in the solution space. Due to the stochastic nature of heuristic algorithms, it's necessary to perform stability analysis of heuristic ensemble classifiers. In this paper, Multi-Objective Inclined Planes Optimization (MOIPO) algorithm, as a novel multi-objective technique, is used to design ensemble classifiers and the performance of created ensemble is compared with ensemble designed by Multi-Objective Particle Swarm Optimization (MOPSO) algorithm. Experimental results confirm the supremacy of MOIPO for designing ensemble classifiers. So, in the next step, for the first time, the stability of this ensemble classifier is analyzed by using statistical method and suitable model for stability analysis is specified.
其他摘要：Some important topics, which affects directly on the performance of the designed ensemble classifier, inflict a complex search space with high dimensions on the researcher. So, heuristic algorithms can be applied to find best solutions because of their capability of efficient search in the solution space. Due to the stochastic nature of heuristic algorithms, it's necessary to perform stability analysis of heuristic ensemble classifiers. In this paper, Multi-Objective Inclined Planes Optimization (MOIPO) algorithm, as a novel multi-objective technique, is used to design ensemble classifiers and the performance of created ensemble is compared with ensemble designed by Multi-Objective Particle Swarm Optimization (MOPSO) algorithm. Experimental results confirm the supremacy of MOIPO for designing ensemble classifiers. So, in the next step, for the first time, the stability of this ensemble classifier is analyzed by using statistical method and suitable model for stability analysis is specified.
关键词：Pattern Recognition;Machine Learning and Data Mining;Classification and Clustering;Statistical and non linear methods
