A hybrid model of fuzzy ARTMAP and genetic algorithm for data classification and rule extraction
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- 作者：Farhad Pourpanaha ; farhad.086@gmail.com" class="auth_mail" title="E-mail the corresponding author ; Chee Peng Limb ; cplim123@yahoo.com" class="auth_mail" title="E-mail the corresponding author ; chee.lim@deakin.edu.au" class="auth_mail" title="E-mail the corresponding author ; Junita Mohamad Saleha ; jms@usm.my" class="auth_mail" title="E-mail the corresponding author
- 关键词：Fuzzy ARTMAP ; Reinforcement learning ; Q-learning ; Data classification ; Rule extraction ; Genetic algorithm
- 刊名：Expert Systems with Applications
- 出版年：2016
- 出版时间：1 May 2016
- 年：2016
- 卷：49
- 期：Complete
- 页码：74-85
- 全文大小：1006 K
文摘
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A hybrid model (QFAM-GA) for data classification and rule extraction is proposed.
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Fuzzy ARTMAP (FAM) with Q-learning is first used for incremental learning of data.
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A Genetic Algorithm (GA) is then used for feature selection and rule extraction.
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Pruning is used to reduce the network complexity and to facilitate rule extraction.
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The results show QFAM-GA can provide useful if-then rule to explain its predictions.