Efficient Learning for Test Feature Classifier by Overlap Index List

Abstract

This paper presents a novel low cost learning algorithm for Test Feature Classifier by use of Overlap Index List (OIL). In general, classifiers need a lot of training data for realizing the high performance, which causes much computation time. The proposed algorithm by OIL can keep search and check elemental combinatorial features from lower dimensions up to higher ones. Classification problems in real industrial inspection lines have been solved by the proposed algorithm, and large amounts of reduction in computation time could be obtained.