Generating High-Dimensional Prototypes with a Classifier System by Evolving in Latent Space
Naoya Yatsu, Hiroki Shiraishi, Hiroyuki Sato, Keiki Takadama · Proceedings of the Genetic and Evolutionary Computation Conference Companion · 2024
Prototype Generation mainly generates classifiable instances in the observation space but often faces the curse of dimensionality as it directly searches high-dimensional spaces with metaheuristics such as genetic algorithms. To mitigate this problem, this paper proposes a novel PG method called kNNUCS-PG w/ LS-OvL. In detail, the proposed method generates high-dimensional prototypes using LS-OvL, which indirectly learns prototypes in the observation space by evolving in latent space for kNNUCS-PG, one of the sUpervised Classifier Systems with a point representation representing a prototype. Through the intensive experiments on MNIST and fashion-MNIST datasets have revealed the following implications: (1) the classification accuracy of kNNUCS-PG w/ LS-OvL is higher than that of the conventional prototype generation methods, showing a statistically significant difference; (2) the number of prototypes of kNNUCS-PG w/ LS-OvL is smaller than that of kNNUCS w/o LS-OvL ; and (3) based on (1) and (2), the indirect prototype generation through dimensionality reduction by VAE is effective in high-dimensional prototypes.