RNBL-MN: a recursive naive bayes learner for sequence classification

Authors:
Dae-Ki Kang;Adrian Silvescu;Vasant Honavar
Affiliations:
Artificial Intelligence Research Laboratory, Department of Computer Science, Iowa State University, Ames, IA;Artificial Intelligence Research Laboratory, Department of Computer Science, Iowa State University, Ames, IA;Artificial Intelligence Research Laboratory, Department of Computer Science, Iowa State University, Ames, IA
Venue:
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Year:
2006

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Abstract

Naive Bayes (NB) classifier relies on the assumption that the instances in each class can be described by a single generative model. This assumption can be restrictive in many real world classification tasks. We describe RNBL-MN, which relaxes this assumption by constructing a tree of Naive Bayes classifiers for sequence classification, where each individual NB classifier in the tree is based on a multinomial event model (one for each class at each node in the tree). In our experiments on protein sequence and text classification tasks, we observe that RNBL-MN substantially outperforms NB classifier. Furthermore, our experiments show that RNBL-MN outperforms C4.5 decision tree learner (using tests on sequence composition statistics as the splitting criterion) and yields accuracies that are comparable to those of support vector machines (SVM) using similar information.