Parsimonious Naive Bayes
Citation: Proceedings of the 2014 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 2, pages 355–359 (2014)
Abstract. We describe our submission to the AAIA'14 Data Mining Competition, where the objective was to reach good predictive performance on text mining classification problems while using a small number of variables. Our submission was ranked 6th, less than 1\% behind the winner. We also present an empirical study on the trade-off between parsimony of the representation and accuracy, and show how good performance can be obtained quickly and efficiently.