RNBL-MN: A recursive Naive Bayes learner for sequence classification

Dae Ki Kang, Adrian Silvescu, Vasant Honavar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 10th Pacific-Asia Conference, PAKDD 2006, Proceedings
PublisherSpringer Verlag
Number of pages10
ISBN (Print)3540332065, 9783540332060
StatePublished - 2006
Event10th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2006 - Singapore, Singapore
Duration: Apr 9 2006Apr 12 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3918 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other10th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2006

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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