Using SVM with Adaptively Asymmetric MisClassification Costs for Mine-Like Objects Detection

Abstract

Real world data mining applications such as Mine Countermeasure Missions (MCM) involve learning from imbalanced data sets, which contain very few instances of the minority classes and many instances of the majority class. For instance, the number of naturally occurring clutter objects (such as rocks) that are detected typically far outweighs the relatively rare event of detecting a mine. In this paper we propose support vector machine with adaptive asymmetric misclassification costs (instances weighted) to solve the skewed vector spaces problem in mine countermeasure missions. Experimental results show that the given algorithm could be used for imbalanced sonar image data sets and makes an improvement in prediction performance.