A new Centroid-Based Classification model for text categorization

Abstract

The automatic text categorization technique has gained significant attention among researchers because of the increasing availability of online text information. Therefore, many different learning approaches have been designed in the text categorization field. Among them, the widely used method is the Centroid-Based Classifier (CBC) due to its theoretical simplicity and computational efficiency. However, the classification accuracy of CBC greatly depends on the data distribution. Thus it leads to a misfit model and also has poor classification performance when the data distribution is highly skewed. In this paper, a new classification model named as Gravitation Model (GM) is proposed to solve the class-imbalanced classification problem. In the training phase, each class is weighted by a mass factor, which can be learned from the training data, to indicate data distribution of the corresponding class. In the testing phase, a new document will be assigned to a particular class with the max gravitational force. The performance comparisons with CBC and its variants based on the results of experiments conducted on twelve real datasets show that the proposed gravitation model consistently outperforms CBC together with the Class-Feature-Centroid Classifier (CFC). Also, it obtains the classification accuracy competitive to the DragPushing (DP) method while it maintains a more stable performance. Thus, the proposed gravitation model is proved to be less over-fitting and has higher learning ability than CBC model.