A method to boost naïve Bayesian classifiers

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2336, Issue , 2002, Pages 115-122

  Diao, Lili ^a   Hu, Keyun ^a   Lu, Yuchang ^a   Shi, Chunyi ^a  

^a TSINGHUA UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

DECISION TREES; LEARNING SYSTEMS;

BASE CLASSIFIERS; BAYESIAN CLASSIFIER; BOOSTING ALGORITHM; BOOTSTRAP METHOD; IMPROVE PERFORMANCE; NAIVE BAYESIAN CLASSIFIER; OPTIMAL PERFORMANCE; TRAINING SAMPLE;

DATA MINING;

EID: 84945265631     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-47887-6_11     Document Type: Conference Paper

References (8)

1. Freund, Y., Schapire, R.: A Decision-theoretic Generalization of On-line Learning and an Application to Boosting. Journal of Computer and System Sciences (1997), 55(1), 119-139
2. Ting, K., Zheng, Z.: Improving the Performance of Boosting for Naive Bayesian Classification. School of Computing and Mathematics, Deakin University (2000)
3. Breiman, L.: Bagging Predictors. Machine Learning (1996), 24(2), 123-140
4. Bremain, L.: Bias, Variance, and Arcing Classifiers. Machine Learning (2000)
5. Quinlan, J.R.: Mini-Boosting Decision Trees. AI Access Foundation and Morgan Kaufmann Publishers (1998)
6. Dietterich, T.G.: Machine Learning Research. AI Magazine (1997), 18(4), 97-136
7. McCallum, A., Nigam, K.: A Comparison of Event Models for Naive Bayesian Text Classification. Just Research. 4616 Henry Street Pittsburgh, PA 15213 (1999)
8. Schapire, R., Singer, Y.: BoosTexter: A Boosting-based System for Text Categorization. Machine Learning (2000), 39(2/3), 135-168