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首页> 外文会议>18th International Symposium on Computer and Information Sciences - ISCIS 2003; Nov 3-5, 2003; Antalya, Turkey >Text Categorization with ILA
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Text Categorization with ILA

机译：使用ILA进行文本分类

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摘要

The sudden expansion of the web and the use of the internet has caused some research fields to regain (or even increase) its old popularity. Of them, text categorization aims at developing a classification system for assigning a number of predefined topic codes to the documents based on the knowledge accumulated in the training process. We propose a framework based on an automatic inductive classifier, called ILA, for text categorization, though this attempt is not a novel approach to the information retrieval community. Our motivation are two folds. One is that there is still much to do for efficient and effective classifiers. The second is of ILA's (Inductive Learning Algorithm) well-known ability in capturing by canonical rules the distinctive features of text categories. Our results with respect to the Reuters 21578 corpus indicate (1) the reduction of features by information gain measurement down to 20 is essentially as good as the case where one would have more features; (2) recall/precision breakeven points of our algorithm without tuning over top 10 categories are comparable to other text categorization methods, namely similarity based matching, naive Bayes, Bayes nets, decision trees, linear support vector machines, steepest descent algorithm.
机译：Web的突然扩展和Internet的使用已导致一些研究领域重新获得（甚至增加了）其古老的知名度。其中，文本分类旨在开发一种分类系统，用于基于训练过程中积累的知识为文档分配多个预定义的主题代码。我们提出了一个基于自动归纳分类器（称为ILA）的框架，用于文本分类，尽管这种尝试对于信息检索社区而言并不是一种新颖的方法。我们的动机有两个方面。一个是高效的和有效的分类器还有很多工作要做。第二个是ILA（归纳学习算法）以规范的规则捕获文本类别的独特特征的众所周知的能力。我们对路透社21578语料库的研究结果表明：（1）通过将信息增益测量降低到20来减少特征，基本上与拥有更多特征的情况一样好； （2）在不调整前10个类别的情况下，我们算法的召回/精确盈亏平衡点可与其他文本分类方法相媲美，即基于相似度的匹配，朴素贝叶斯，贝叶斯网络，决策树，线性支持向量机，最速下降算法。

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