【摘要】：垃圾短信過濾是文本分類的一種,將用戶收到的短信分為正常短信和垃圾短信,從而實現(xiàn)對垃圾短信的屏蔽。在樸素貝葉斯分類算法的基礎上進行改進,針對短信內(nèi)容較短包含信息不足的特點,引入同義詞集對短信中特征詞進行擴展,降低同義特征詞分散給分類帶來的負面影響。同時針對垃圾短信自身包含的特殊信息,提出模式概念,采用模式概念替換具有相同模式的特征詞,使垃圾短信的特征更加集中,增強分類算法對垃圾短信的鑒別能力,最后通過實驗對樸素貝葉斯算法以及改進后算法的分類性能進行了分析,驗證了改進后算法的有效性。
[Abstract]:Spam short message filtering is a kind of text classification, which classifies the SMS received by users into normal SMS and spam SMS, so that the spam SMS can be shielded. Based on the naive Bayes classification algorithm, aiming at the lack of information in short message, the synonym set is introduced to extend the feature words in short message, so as to reduce the negative effect of synonym dispersion on the classification. At the same time, aiming at the special information contained in spam message itself, the concept of pattern is put forward, and the concept of pattern is used to replace the feature word with the same pattern, so that the feature of spam short message is more concentrated, and the ability of classification algorithm to identify spam message is enhanced. Finally, the classification performance of the naive Bayes algorithm and the improved algorithm are analyzed through experiments, and the effectiveness of the improved algorithm is verified.
【作者單位】： 南京師范大學泰州學院信息工程學院;
【基金】：江蘇省大學生創(chuàng)新訓練計劃項目(201613843015Y) 教育部—Google2014年校企合作產(chǎn)學合作項目(PO640068)
【分類號】：TP391.1

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