Practical application of associative classifier for document classification

Abstract

In practical text classification tasks, the ability to interpret the classification result is as important as the ability to classify exactly. The associative classifier has favorable characteristics, rapid training, good classification accuracy, and excellent interpretation. However, the associative classifier has some obstacles to overcome when it is applied in the area of text classification. First of all, the training process of the associative classifier produces a huge amount of classification rules, which makes the prediction for a new document ineffective. We resolve this by pruning the rules according to their contribution to correct classifications. In addition, since the target text collection generally has a high dimension, the training process might take a very long time. We propose mutual information between the word and class variables as a feature selection measure to reduce the space dimension. Experimental classification results using the 20-newsgroups dataset show many benefits of the associative classification in both training and predicting.