A wrapper-based feature selection approach using Bees Algorithm for a wood defect classification system

Abstract

Identifying defects and classifying them according to some predefined classes is common in many manufacturing processes. The basis of such approach depends on a set of features extracted from all the classes and using them to train a classifier and then use it to determine the class to which the unseen data belongs to, with a reasonable accuracy. Hence the performance of the classifier depends on the features' ability to discriminate between the good or normal and the defects. Therefore, one way of improving the classifier is to select the most appropriate features from a given feature set for the purpose of training and testing so that, at the end, better results can be achieved overall. In this paper, a novel wrapper-based feature selection approach using Bees Algorithm for the application of wood defect classification is presented. Bees Algorithm is a swarm-based optimisation technique mimicking the foraging behaviour of honey bees found in nature. In order to demonstrate the wrapper-based feature selection procedure a Minimum Distance Classifier (MDC) is used in this study. However, the method can be applied to any application using some other classifier. The study shows that, on average, a 10% improvement is achieved when a reduced sub-set of 12 features selected using the proposed wrapper-based method with Bees Algorithm is used in training and testing the MDC when compared to using the original full set of 17 features. The rejected features correspond to outliers.