Open AccessResearch on Spam Filtering Technology Based on New Mutual Information Feature Selection Algorithm
Author(s) -
Kunfu Wang,
Wanfeng Mao,
Wei Feng,
Hui Wang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1673/1/012028
Subject(s) - mutual information , naive bayes classifier , feature selection , conditional mutual information , computer science , conditional independence , artificial intelligence , pattern recognition (psychology) , statistical classification , feature (linguistics) , data mining , dimensionality reduction , algorithm , support vector machine , linguistics , philosophy
Aiming at the deficiency of traditional mutual information algorithm in feature selection, this paper proposes a weighted naive Bayesian algorithm based on improved mutual information, called imi-wnb algorithm. In the feature selection stage, the word frequency factor and the difference factor between classes are introduced to improve the traditional mutual information algorithm to achieve feature dimension reduction. In the process of classification, the value of IMI is introduced to weight the attributes of naive Bayes algorithm, which partly eliminates the influence of conditional independence assumption of naive Bayes algorithm on classification, and improves the efficiency and stability of spam classification.