SPaC-NF: A classifier based on sequential patterns with high netconf

摘要

In this paper, an accurate Sequential-Patterns based Classifier, called SPaC-NF, is proposed. SPaC-NF introduces 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 strategy for generating the Sequential-Patterns based Rules and a new way of ordering them based on their rule sizes and Netconf values is introduced in SPaC-NF. The ordering strategy together with the "Best K rules" satisfaction mechanism allows SPaC-NF to have better accuracy than SVM, J48, NaiveBayes and PART classifiers. Most of these classifiers were evaluated using Weka, a popular suite of machine learning software. The experiments were done using ten-fold cross-validation, reporting the average over the ten folds. Similar to other works, experiments were conducted using several document collections, three in our case: AFP, TDT and Reuter.