基于模糊积分和遗传算法的分类器组合算法

将多个分类器进行组合能提高分类精度。基于模糊测度的Sugeno和Choquet积分具有理想的特性,因此该文利用其进行分类器组合。然而在实际中难以求得模糊测度。该文利用两种方法求取模糊测度,一是分类器对样本数据的分类能力,另一种是根据遗传算法。这两种方法均考虑了每个分类器对不同类的分类能力不同这一经验知识。实验中对UCI中的几个数据库进行了测试,同时将该组合方法应用于一多传感器融合工件识别系统。测试结果表明了该算法是一种计算简便、精度较高的分类器组合方法。

An Algorithm for Combining Classifiers Based on Fuzzy Integral and Genetic Algorithms

Combination of many different classifiers can improve classification accuracy.Sugeno and Choquet integrals with respect to the fuzzy measure possess many desired properties,so in this paper they are used to combine multiple neural network classifiers.However,it is difficult to determine fuzzy measures in real problems.This paper presents two methods,one is to assign the degree of importance of each network based on how good these networks classify each class of the training data,the other is by genetic algorithms (GAs ),to obtain fuzzy measures,each taking into account the intuitive idea that each classifier always possesses different classification ability for each class.In the experiment ,several databases in UCI repository are tested using these combination schemes.They are also applied to a multisensor fusion system for workpiece identification.Experimental results confirm the superiority of these presented methods.