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Khalpada, Kruti
- Incremental Discretization for Naïve Bayes Learning with Optimum Binning
Authors
1 Charotar University of Science and Technology, Changa, Gujrat, IN
2 Charotar University of Science and Technology Changa, Gujrat, IN
3 Department of Computer Engineering, Dharamsinh Desai University, Nadiad, Gujarat, IN
Source
Data Mining and Knowledge Engineering, Vol 3, No 4 (2011), Pagination: 266-271Abstract
Incremental Flexible Frequency Discretization (IFFD) is a recently proposed discretization approach for Naïve Bayes (NB).IFFD performs satisfactory by setting the minimal interval frequency for discretized intervals as a fixed number. In this paper, we first argue that this setting cannot guarantee that the selecting MinBinSize is on always optimal for all the different datasets. So the performance of Naïve Bayes is not good in terms of classification error. We thus proposed a sequential search method for NB: named Optimum Binning. Experiments were conducted on 4 datasets from UCI machine learning repository and performance was compared between NB trained on the data discretized by OB, IFFD, and PKID.
Keywords
Discretization, Naïve Bayes, Optimum Binning.
- Ant Colony Optimization Approach for TTP with Balanced Intensification and Diversification
Authors
1 Charotar University of Science and Technology, Charusat, Changa, IN
2 Charotar University of Science and Technology, Charusat, Changa, IN
3 Dharmsinh Desai University, D.D.U., Nadiad, IN