文本分類是文本數(shù)據(jù)挖掘中一個(gè)非常重要的技術(shù)，已經(jīng)被廣泛地應(yīng)用于信息管理、搜
索引擎、推薦系統(tǒng)等多個(gè)領(lǐng)域?，F(xiàn)有的文本分類方法，大多是基于向量空間模型的算法。這些算法很難適用于大規(guī)模的文本數(shù)據(jù)集。為此，我們提出了一種基于遺傳算法和信息熵的文本分類規(guī)則抽取方法。在該方法中，信息熵技術(shù)用來(lái)輔助遺傳算法初始種群的生成。遺傳算法和信息熵的有效集成，極大地提高了該混合方法的分類效率。實(shí)驗(yàn)結(jié)果表明，本文方法適用于大規(guī)模文本數(shù)據(jù)集；該方法提取規(guī)則的分類正確率較高，分類速度較快。
關(guān)鍵詞：文本分類；遺傳算法；信息熵；文本挖掘
Extraction Method of Text Classification Rule Based on Genetic Algorithm and Information
Entropy ZHOU Guoping 1, 2, PENG Xiangmei 1, HUANG Guobing 1
(1. Xinyu College, Xinyu, 338031, China;
2. Department of computer science, Nanchang College, Xinyu, 338031, China)
Abstract：Text classification is a very important technique in the field of text mining, and it has been widely applied to the information management, search engine, recommendation systems, and some other fields. Most classification methods are based on vector models, these approaches are highly complicated on computation, and cannot be used on the occasion of classifying a large number of samples. For this reason, a hybrid approach combining genetic algorithm with information entropy is presented for text classification rule extraction. In this hybrid approach, the information entropy technique is applied to assist the generation of initial populations for genetic algorithm. The classification performance of the proposed approach has been improved largely by integrating genetic algorithm with information entropy effectively. The proposed approach can be applied to classify a large number of samples. Experimental results show that both the accuracy and the speed of categorization are high.
Keywords：Text classification; genetic algorithm; information entropy; text mining