A two-class text categorization method

active learning negative selection text categorization (ALNSTC) algorithm

based on active learning (AL) method and negative selection (NS) algorithm

is proposed for the problem of spam proliferation. The positive user interest set and the negative user interest set are established according to a small number of labeled samples. And the sampling engine (SE) of AL method is improved by the autologous anomaly detection mechanism of the NS algorithm. The two-way user interest sets are used as detectors

and a new sample set is employed as a self-set. The above two sets are matched with Hamming match rules. The classification process of each sample set is able to update the two user interest sets. The proposed algorithm is carried out with a full-scale test on six common spam corpus

which are selected as experimental material

and analyzed and compared with other five state-of-the-art spam classification methods

which are quick online spam identification (QOSI) method

semi-supervised collaboration classification algorithm with enhanced difference (DSCC)

dynamic web spam filtering (WSF2) method

multilevel spam filtering algorithm based on artificial immunity (MSFA-AI)

and integrated multi-field learning (MFL) method

in different evaluation metrics

such as precision

recall

ROC curve

categorization running time and the labeled number of spam. The results show that the proposed method has better precision rate

recall rate

classification accuracy

and can reduce the artificial labeled number of spam samples. It is advantageous to enhance the classification capacity of the algorithm that the user preferences are converted into positive and negative user interest sets. In addition

the user labeled number is reduced when unknown category features are obtained by the exception detection mechanism.