Comparison of Pattern Classification Methods in Discrimination of Inferior Shijimi Clams Based on Acoustic Signals

Abstract

In this paper, we compare the pattern classification methods to discriminate the inferior shijimi clams with aim at development of the selecting device for shijimi clams. In our framework, the feature vectors are extracted based on frequency analysis of the acoustic signal that occurs in hitting shijimi clams themselves, and then shijimi clams are classified by the pattern classification methods such as decision tree learning, multi-layer perceptron, k-nearest-neighbors classification method and support vector machine. The experimental results indicate that multi-layer perceptron shows the best performance among the four pattern classification methods. Also we confirm all four pattern classification methods show sufficiently high accuracy. Therefore, we can show the effectiveness of our framework.