%0 Journal Article
%T SAT-FOIL+: Sentence-Level Association Based Text Classification<br>SAT-FOIL＋：基于句子级关联的文本分类
%A FENG Yu-Cai
%A LI Qu
%A HE Yu
%A FENG Jian-Lin
%A <br>冯玉才
%A 李曲
%A 何玉
%A 冯剑琳
%J 计算机科学
%D 2005
%I 
%X While previous association based methods mainly mined frequently co-occurring words (frequent itemsets) at the document-level, the basic semantic unit in a document is actually a sentence. Words within the same sentence are typically more semantically related than words that just appear in the same document. Our proposed SAT-FOIL views a sentence rather than a document as a transaction. In this paper we proposed new score models to get the im- proved algorithm SAT-FOIL . The effectiveness of our proposed SAT-FOIL method has been demonstrated not only better than our former algorithm SAT-FOIL but also comparable to well-known alternatives and much better than previous document-level association based methods by extensive experimental studies using popular benchmark text collections Reuters. In addition, SAT-FOIL has inherent readability and refinability of acquired classification rules.
%K Text classification
%K Sentence-level
%K Association rules
%K Frequent itemsets<br>文本分类
%K 句子级别
%K 关联规则
%K 频繁项目集
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=64A12D73428C8B8DBFB978D04DFEB3C1&aid=E01EB2842A77599F&yid=2DD7160C83D0ACED&vid=9971A5E270697F23&iid=38B194292C032A66&sid=334E2BB8B9A55ABB&eid=D2742EEE6F4DF8FE&journal_id=1002-137X&journal_name=计算机科学&referenced_num=0&reference_num=18