Improving the accuracy of the sequential patterns-based classifiers

Abstract

In this paper, we propose some improvements to the Sequential Patterns-based Classifiers. First, we introduce a new pruning strategy, using the Netconf as measure of interest, that allows to prune the rules search space for building specific rules with high Netconf. Additionally, a new way for ordering the set of rules based on their sizes and Netconf values, is proposed. The ordering strategy together with the “Best K rules” satisfaction mechanism allow to obtain better accuracy than SVM, J48, NaiveBayes and PART classifiers, over three document collections.