Naoya Yatsu
ABSTRACT
To mine high-dimensional rules in Learning Classifier Systems (LCSs) through a reduction of the dimensionality of input data, this paper proposes a novel approach that indirectly learns the rules in the "latent space" based on the rewards of the reconstructed rules in the "observation space". We call this approach Learning Strategy by exploring rules in Observation space via Latent space (LS-OvL), which is based on two rule representations in the observation and latent space. Concretely, LS-OvL explores the rules by searching the latent space as the reduced dimensional input space by an autoencoder and evaluates them in the observation space by reconstructing them from the latent space. Such a design is significant because it prevents the generation of inaccurate rules during the reconstitution process from the latent space to the observation space. Through a comparison LS-OvL with the conventional learning strategy, which explores and evaluates the rules in the only latent space and reconstructs them in the observation space, the experimental results show that (1) LS-OvL outperforms the conventional learning strategy in terms of the acquired reward and the population size, and (2) LS-OvL can generate explainable and classifiable high-dimensional rules.
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Index Terms
- Exploring High-dimensional Rules Indirectly via Latent Space Through a Dimensionality Reduction for XCS
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