Comparing the one-vs-one and one-vs-all methods in benthic macroinvertebrate image classification

Abstract

This paper investigates automated benthic macroinvertebrate identification and classification with multi-class support vector machines. Moreover, we examine, how the feature selection effects results, when one-vs-one and one-vs-all methods are used. Lastly, we explore what happens for the number of tie situations with different kernel function selections. Our wide experimental tests with three feature sets and seven kernel functions indicated that one-vs-one method suits best for the automated benthic macroinvertebrate identification. In addition, we obtained clear differences to the number of tie situations with different kernel funtions. Furthermore, the feature selection had a clear influence on the results. © 2011 Springer-Verlag.