Multi-Label Text Categorization Using a Probabilistic Neural Network

Techniques for categorization and clustering, range from support vector machines, neural networks to Bayesian in- ference and algebraic methods. The k-Nearest Neighbor Algo- rithm (kNN) is a popular example of the latter class of these al- gorithms. Recently, slightly modified versions of support vector machines, kNN and decision trees have been proposed to deal better with multi-label classification problems. In this paper, we also proposed a new version of a Probabilistic Neural Network (PNN) to tackle these kind of problems. This PNN was pro- posed aiming at executing automatic classification of economic activities, which is the focus of this article. Nevertheless, we compared the PNN algorithm against other classifiers. In addi- tion to economic activities database, we applied our algorithm to some other databases found in the literature. In general, our approach surpassed the other algorithms in many metrics typ- ically well known in the literature for the multi-label categ oriza-