Multi-Dimensional Text Classification

This paper proposes a multi-dimensional framework for classifying text documents. In this framework, the concept of multidimensional category model is introduced for representing classes. In contrast with traditional flat and hierarchical category models, the multi-dimensional category model classifies each text document in a collection using multiple predefined sets of categories, where each set corresponds to a dimension. Since a multi-dimensional model can be converted to flat and hierarchical models, three classification strategies are possible, i.e., classifying directly based on the multi-dimensional model and classifying with the equivalent flat or hierarchical models. The efficiency of these three classifications is investigated on two data sets. Using k-NN, naïve Bayes and centroidbased classifiers, the experimental results show that the multi-dimensional-based and hierarchical-based classification performs better than the flat-based classifications.