多目标优化算法在多分类中的应用研究

 Cai等人用多目标粒子群算法(MOPSO)优化多目标聚类学习和分类学习框架(MSCC)的多目标问题时,种群只能得到少量的非支配解,不利于种群优化.而在此情况下,NSGA-II由于采用了Pareto排序的方法,种群中会保留大量优秀的支配解,有利于种群优化,所以本文引进了NSGA-II优化MSCC框架的多目标问题.通过对数据集的测试,验证了在NSGA-II的优化下,对于大多数测试问题,MSCC框架设计的分类器的最大分类正确率高于MOPSO优化MSCC框架的结果.进而对实验结果做了进一步分析,发现了最大正确率不随多目标优化算法的优化过程而提高的问题.

Research of Multi-Objective Optimization Algorithms' Application in Multi-Class Classification

When Multi-objective Particle Swarm Optimization (MOPSO) optimizes the multi-objective problems of the multiobjective simultaneous learning framework (MSCC),there are only a few nondominated solutions in MOPSO population.In this case,NSGA-II can keep a lot of good dominated solutions in the population,which will help the population optimize,so this paper brought in NSGA-II as the optimization algorithm.The results of experiments show that,under the optimization of NSGA-II,MSCC framework can get better multi-class classifiers.However,dominated solutions can get better classifiers than nondominated solutions.By observing the changing curves of the maximum classification accuracy rate following with the optimization of populations,this paper found that,when dealing with most of the data sets,the maximum accuracy is not improved following the optimization of populations.