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一种用于文本分类规则归纳的遗传算法

A Genetic Algorithm for Text Classification Rule Induction
课程网址: http://videolectures.net/ecmlpkdd08_pietramala_agaf/  
主讲教师: Adriana Pietramala; Veronica L. Policicchio
开课单位: 卡拉布里亚大学
开课时间: 2008-10-10
课程语种: 英语
中文简介:
本文提出了一种称为Olex-GA的遗传算法,用于归纳形式为“分类文档$d$”的基于规则的文本分类器,如果$t_1在d$中或。。。或d$中的$t_n而非(d$中为$t_{n+1}或…或d$的$t_{n+m})持有“”,其中每个$t_i$都是一个项。Olex-GA依赖于有效的emph{每个个体有几个规则}二进制表示,并使用$F$-测度作为适应度函数。 所提出的方法在标准测试集Reuters和Ohsumed上进行了测试,并与几种分类算法(即Naive Bayes、Ripper、C4.5、SVM)进行了比较。实验结果表明,它在两个数据集上都取得了非常好的性能,与所评估的分类器相比具有竞争力(在某些情况下甚至优于)。 注: 该论文中描述的规则归纳系统Olex GA的原型可在以下地址获得
课程简介: This paper presents a Genetic Algorithm, called Olex-GA, for the induction of rule-based text classifiers of the form ``classify document $d$ under category $c$ if $t_1 in d$ or ... or $t_n in d$ and not ($t_{n+1} in d$ or ... or $t_{n+m} in d$) holds'', where each $t_i$ is a term. Olex-GA relies on an efficient emph{several-rules-per-individual} binary representation and uses the $F$-measure as the fitness function. The proposed approach is tested over the standard test sets Reuters and Ohsumed and compared against several classification algorithms (namely, Naive Bayes, Ripper, C4.5, SVM). Experimental results demonstrate that it achieves very good performance on both data collections, showing to be competitive with (and indeed outperforming in some cases) the evaluated classifiers. Note: A prototype of the rule induction system Olex-GA described in that paper is available at the address 
关 键 词: 文本分类; 遗传算法; 归纳形式
课程来源: 视频讲座网
数据采集: 2023-07-24:chenxin01
最后编审: 2023-07-24:chenxin01
阅读次数: 12