Ranking Tokens with Class Label Frequencies for Medical Article Classification

In this paper, a new method for medical article classification is proposed based on exploiting information from local and global class label frequencies in training corpus. The proposed method partially overcomes the low accuracy rate of KNN classifier. First, it uses a lexical approach to identify tokens in the medical document article and then, it uses local and global class label frequencies in a sophisticated way similar to traditional tf-idf weighting scheme to devise the weighted function in classification process. The evaluation experiments on the collection of medical documents, called Ohsumed, show that the method proposed here significantly outperforms traditional KNN classification.