JACIII Vol.21 No.5 pp. 856-867
doi: 10.20965/jaciii.2017.p0856


XCSR Learning from Compressed Data Acquired by Deep Neural Network

Kazuma Matsumoto*, Takato Tatsumi*, Hiroyuki Sato*, Tim Kovacs**, and Keiki Takadama*

*The University of Electro-Communications
1-5-1 Chofugaoka, Chofu, Tokyo 182-8585, Japan

**The University of Bristol
MVB, Woodland Rd., Bristol, BS8 1UB, United Kingdom

March 21, 2017
July 21, 2017
September 20, 2017
LCS, XCS, XCSR, neural network, deep learning

The correctness rate of classification of neural networks is improved by deep learning, which is machine learning of neural networks, and its accuracy is higher than the human brain in some fields. This paper proposes the hybrid system of the neural network and the Learning Classifier System (LCS). LCS is evolutionary rule-based machine learning using reinforcement learning. To increase the correctness rate of classification, we combine the neural network and the LCS. This paper conducted benchmark experiments to verify the proposed system. The experiment revealed that: 1) the correctness rate of classification of the proposed system is higher than the conventional LCS (XCSR) and normal neural network; and 2) the covering mechanism of XCSR raises the correctness rate of proposed system.

Cite this article as:
K. Matsumoto, T. Tatsumi, H. Sato, T. Kovacs, and K. Takadama, “XCSR Learning from Compressed Data Acquired by Deep Neural Network,” J. Adv. Comput. Intell. Intell. Inform., Vol.21 No.5, pp. 856-867, 2017.
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