多目标集成分类器的设计与稳定性分析

Q4 Computer Science
Z. Pourtaheri, S. Zahiri, S. M. Razavi
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引用次数: 2

摘要

一些重要的主题直接影响集成分类器的性能,给研究人员带来了复杂的高维搜索空间。因此,启发式算法在解空间中具有高效搜索的能力,可以用于寻找最优解。由于启发式算法的随机性,有必要对启发式集成分类器进行稳定性分析。本文将多目标倾斜平面优化(MOIPO)算法作为一种新颖的多目标技术用于集成分类器的设计,并将所创建的集成与多目标粒子群优化(MOPSO)算法设计的集成进行性能比较。实验结果证实了MOIPO在集成分类器设计中的优越性。因此,在下一步中,首次使用统计方法分析了该集成分类器的稳定性,并指定了适合稳定性分析的模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Design and Stability Analysis of Multi-Objective Ensemble Classifiers
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.
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来源期刊
Electronic Letters on Computer Vision and Image Analysis
Electronic Letters on Computer Vision and Image Analysis Computer Science-Computer Vision and Pattern Recognition
CiteScore
2.50
自引率
0.00%
发文量
19
审稿时长
12 weeks
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