CAJ | 학술논문

不良短信的泛滥，严重影响了社会风气，干扰了人们正常的生活秩序，研发不良短信过滤技术具有相当的实用价值。应用中科院计算所研制开发的ICTCLAS分词系统，结合TFIDF词权度量指标提取关键词，实现短信文本到特征向量的转换，然后采用kNN方法实现短信的类别判断，从而实现不良短信的过滤。另外，针对训练集分布不均衡的情况，应用基于密度的改进方法，较为有效地处理了原来分类结果倾向于大类别样本的情况。实验表明，改进后的方法的准确率约79．18％，比原方法提升了约1．23％。该方法能够比较有效地过滤不良短信，具有一定的实用价值。
불량단신적범람，엄중영향료사회풍기，간우료인문정상적생활질서，연발불량단신과려기술구유상당적실용개치。응용중과원계산소연제개발적ICTCLAS분사계통，결합TFIDF사권도량지표제취관건사，실현단신문본도특정향량적전환，연후채용kNN방법실현단신적유별판단，종이실현불량단신적과려。령외，침대훈련집분포불균형적정황，응용기우밀도적개진방법，교위유효지처리료원래분류결과경향우대유별양본적정황。실험표명，개진후적방법적준학솔약79．18％，비원방법제승료약1．23％。해방법능구비교유효지과려불량단신，구유일정적실용개치。
The overrunning of the unwanted short messages seriously impacts the social ethos and disrupts the normal life order of people .It has considerable practical value to research and develop the filtering technology of harmful short messages .In this paper, ICTCLAS segmentation system developed by the Institute of Computing Technology of CAS is applied to realise the transition of short message text to the eigenvectors in combination with keywords extraction using TFIDF word right metrics , then the kNN method is adopted to realise the discriminant of short messagescategories, thus the filtration of bad short messages is realised .In addition, according to the unbalanced distribution of training set, we apply the density-based improved method to solve the case of original classification results which are prone to the categories of big sample quite efficiently.Experiments show that the accuracy rate of the improved method reaches about 79.18%, a 1.23% increase compared with the originalmethod.This method is able to more effectively filter the unwanted short messages , and has certain practical value .