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Publication - Dr Tim Kovacs

    XCSR based on compressed input by deep neural network for high dimensional data


    Matsumoto, K, Sato, H, Takano, R, Kovacs, TMD, Tatsumi, T & Takadama, K, 2018, ‘XCSR based on compressed input by deep neural network for high dimensional data’. in: GECCO'18: 2018 Genetic and Evolutionary Computation Conference Companion. Association for Computing Machinery (ACM), pp. 1418-1425


    This paper proposes the novel Learning Classifier System (LCS) which can solve high-dimensional problems, and obtain human-readable knowledge by integrating deep neural networks as a compressor. In the proposed system named DCAXCSR, deep neural network called Deep Classification Autoencoder (DCA) compresses (encodes) input to lower dimension information which LCS can deal with, and decompresses (decodes) output of LCS to the original dimension information. DCA is hybrid network of classification network and autoencoder towards increasing compression rate. If the learning is insufficient due to lost information by compression, by using decoded information as an initial value for narrowing down state space, LCS can solve high dimensional problems directly. As LCS of the proposed system, we employs XCSR which is LCS for real value in this paper since DCA compresses input to real values. In order to investigate the effectiveness of the proposed system, this paper conducts experiments on the benchmark classification problem of MNIST database and Multiplexer problems. The result of the experiments shows that the proposed system can solve high-dimensional problems which conventional XCSR cannot solve, and can obtain human-readable knowledge.

    Full details in the University publications repository