分类器(UML)
算法
加权多数算法
计算机科学
基于群体的增量学习
学习分类器系统
对数
文化算法
遗传算法
人工智能
适应度函数
机器学习
人工神经网络
唤醒睡眠算法
数学
数学分析
泛化误差
作者
Liyan Dong,Guangyuan Liu,Senmiao Yuan,Yongli Li,Zhen Li
标识
DOI:10.1109/icicic.2007.214
摘要
The paper addresses the problem of classification. A restricted BAN classifier learning algorithm - GBAN based on genetic algorithm is proposed. Genetic algorithm is used in this new algorithm to study the network structure, this can reduce complexity of calculation substantially. Meanwhile, the network structure of TAN classifier is extended by restricting the complexity of the structure of BAN classifier., and then a restricted BAN classifier is obtained. To learn the structure of this kind classifier, fitness function based on logarithm likelihood and the corresponding genetic operator are designed, network structure code scheme is also designed. As a result, this algorithm can converges on the overall optimal structure. Experimental result shows that GBAN algorithm performs better than TAN algorithm and has a better accuracy when the relationship between attributes of a data set is relatively complicated.
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