Spam short message classifier model based on word terms
Liu Jin-ling · Journal of Computer Applications · 2013
A classifier model based on word terms was proposed to classify Spam Short Messages(SSM).The concept of word-category weight was introduced for representing a word effect of identifying the category a SSM belongs to and a method was put forward to calculate the word-category weight.Based on the word-category weight,a dimension reduction was carried out to get word items set.The Short message-Category Membership Value(SCMV) was used to illustrate how much a SSM belonged to a category,then a classifying algorithm was implemented by computing SCMV and SCMV density.To improve the accuracy of classification and make the word-category weight more reasonable,an word-weight iterative learning procedure was performed.The experimental results show that the proposed model is superior to other classification methods in terms of classification performance and time complexity.