Automatic document classification: the role of interclass similarity

Abstract

The continuous increase of information in digital format requires new methods and techniques to access, collect and organize these volumes of textual information. One of the most widely used techniques to organize information is the automatic classification of documents. Automatic text classification systems have a low efficiency when the classes are very similar, i.e. there is overlap between them, and in this case it is very important to be able to identify those attributes that allow us to separate one class from another. In this paper we present the relationship between overlap between classes and classification accuracy. A public corpus with four classes is used for the evaluation, and each class is further separated by positives and negatives. The results obtained from four subsets with different number of training instances are presented, for each case the similarity plots, the accuracy value and the confusion matrices obtained are presented. The results obtained are very illustrative and show that the higher the similarity between classes, the lower the classification accuracy.