進化的条件判断ネットワークEDENによる画像分類

概要

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Many methods that construct image classification algorithms automatically using evolutionary computation have been studied. Although these classifiers are very effective, several problems have been pointed out. For example, it is difficult to analyze or modify classifiers, and they are too complicated for humans to understand. In this paper, we propose a new method for classification using an evolutionary decision network (EDEN) that emphasizes good human-readability. EDEN automatically constructs an adequate network for classification by combining simple nodes using evolutionary computation. This network is composed of a set of nodes that changes the branches of the decision flow in accordance with the feature values of the input data. We build an effective classifier by optimizing a set of nodes and their threshold values for branching. In experiments, we evaluate EDEN by applying it to image classification problems. The experimental results show EDEN is able to build an effective classifier with a human-readable structure and achieve satisfactory performance.