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A reinforced fuzzy ARTMAP model for data classification

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Abstract

This paper presents a hybrid model consisting of fuzzy ARTMAP (FAM) and reinforcement learning (RL) for tackling data classification problems. RL is used as a feedback mechanism to reward the prototype nodes of data samples established by FAM. Specifically, Q-learning is adopted to develop the hybrid model known as QFAM. A Q-value is assigned to each prototype node, which is updated incrementally based on the prediction accuracy of the node pertaining to each data sample. To evaluate the performance of the proposed QFAM model, a series of experiments with benchmark problems and a real-world case study, i.e., human motion recognition, are conducted. The bootstrap method is used to quantify the results with the 95% confidence interval estimates. The results are also compared with those from FAM as well as other models reported in the literature. The outcomes indicate the effectiveness of QFAM in tackling data classification tasks.

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Acknowledgements

This work is partially supported by the Science and Technology Innovation Committee of Shenzhen City (No. CKFW2016041415372174) and (No. GJHZ201703141144) and the National Natural Science Foundation of China (No. 6177319).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China

    Farhad Pourpanah & Qi Hao

  2. Institute for Intelligent Systems Research and Innovation, Deakin University, Waurn Ponds, Australia

    Chee Peng Lim

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  1. Farhad Pourpanah
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  2. Chee Peng Lim
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  3. Qi Hao
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Correspondence to Farhad Pourpanah.

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Pourpanah, F., Lim, C.P. & Hao, Q. A reinforced fuzzy ARTMAP model for data classification. Int. J. Mach. Learn. & Cyber. 10, 1643–1655 (2019). https://doi.org/10.1007/s13042-018-0843-4

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