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Abstract: This paper introduces an algorithm that combines nave Bayes classification with feature weighting. Most of the related approaches to feature transformation for nave Bayes suggest various heuristics and non-exhaustive search strategies for selecting a subset of features with which nave Bayes performs better than with the complete set of features. In contrast, the algorithm introduced in this paper employs feature weighting performed by a support vector machine. The weights are... (Update)
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BibTeX entry: (Update)
@misc{ thomas-wbcsvm,
author = "Thomas Grtner Thomas",
title = "WBCSVM : Weighted Bayesian Classification based on Support Vector Machines",
url = "citeseer.ist.psu.edu/739546.html" }
Citations (may not include all citations):2177 Programs for Machine Learning (context) - Quinlan - 1993
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55 A Review and Empirical Evaluation of Feature-Weighting Metho.. - Wettschereck, Aha et al. - 1997
26 and Effective Rule Learner (context) - Cohen, Singer - 1999
15 Correlation based Feature Selection for Machine Learning - Hall - 1999
9 Combining nearest neighbor classifiers through multiple feat.. - Bay - 1998
1 Kernel-Based Feature Space Transformation in Inductive Logic.. (context) - Grtner - 2000
1 Decomposing probability distributions over structured indivi.. (context) - Flach, Lachiche - 2000
1 Benchmarking attribute selection techniques for data mining - Hall, Holmes - 2000
1 Data Mining - Practical Machine Learning Tools and Technique.. (context) - Review, pp et al. - 2000
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