Artificial neural network — Naïve bayes fusion for solving classification problem of imbalanced dataset
Incorporating knowledge from domain expert to a classifier is one of the techniques which require to be considered in solving imbalanced dataset problems. In this study, the proposed technique is a development to extend the process for imbalanced dataset where the individual classification system ha...
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| Main Authors: | , , , |
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| Format: | Book Section |
| Published: |
IEEE
2011
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/29593/ http://dx.doi.org/10.1109/ICMSAO.2011.5775584 |
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| Summary: | Incorporating knowledge from domain expert to a classifier is one of the techniques which require to be considered in solving imbalanced dataset problems. In this study, the proposed technique is a development to extend the process for imbalanced dataset where the individual classification system has already been designed for balanced data set. This paper introduces a methodology and preliminary results which are used to investigate whether the proposed approach is possible to improve a classifier's performance when domain expert is employed to the nai¨ve bayes classifier. Domain expert is an additional knowledge which is produced by expert system (neural network) and then become an additional input to the nai¨ve bayes classifier. By using several benchmark data sets from the UCI Machine Learning Repository, the results of the proposed technique show an improvement as compared to the conventional nai¨ve bayes classifier. |
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