Email binary classification algorithm based on active learning and negative selection

Abstract

The invention relates to an email binary classification algorithm based on active learning and negative selection. The email binary classification algorithm is characterized in that user bidirectionalinterest sets are first established according to marked email sets; an email binary classification algorithm is constructed by using an abnormal detection mechanism in a negative selection algorithm,and to-be-classified email sets serve as self-sets to perform matching detection; finally email classification results are obtained by using matching results, and the user bidirectional interest setis updated; an active learning method and the negative selection algorithm are applied to spam filtering, the to-be-classified email sets serve as the self-sets, user positive and negative interest sets constructed from existing labeled email sets serve as detectors, all email key feature sets screened by a key feature selection algorithm serve as classified objects, and finally classification results of the email sets are obtained through an anomaly detection matching mechanism. The algorithm performs bidirectional binary matching detection on the email sets through positive and negative interest sets, and a new idea is provided for the spam filtering method.

Description

Technical field [0001] The invention relates to a two-class classification algorithm for mail based on active learning and negative selection. The positive and negative interest sets of users are constructed by using the active learning method in the machine learning method, and the self-set and detection mechanism in the negative learning algorithm are combined to realize Fast and efficient spam filtering belongs to the cross-technology application field of machine learning and text classification. Background technique [0002] Text classification technology is a technology that uses computer programs to automatically classify and mark text collections (or other entities or objects) according to a certain classification system or standard, so that people can better understand, coordinate, and reasonably use network text information. In recent years, text classification technology has been widely used in fields such as mail classification, information filtering, and text corpus c...

AI Technical Summary

Problems solved by technology

[0004] Defects of existing methods and inventions: 1) Due to the high economic cost of expert labeling and the inability to effectively label large-scale problems, the number of unlabeled sample data is huge and easy to obtain; 2) Traditional machine learning in existing solutions Algorithms, especially supervised learning algorithms, must label a large number of sample data, otherwise the generalization performance will be low; 3) For spam filtering, the user's personal preferences have a greater impact on the classification results; 4) When manually labeling samples online, Experts cannot directly choose the best labeling time

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