Niko Jay Murrell

Expert-prescribed weighting for support vector machine classification

Support Vector Machines (SVM) are a family of algorithms that are used in classification and regression tasks. Often, multiple SVMs are combined in a coding scheme to provide multi-class classification capabilities. Generally, multi-class classification systems are evaluated on their accuracy of producing a correct coding by using test data and successful predictions are counted as a percentage of the whole, assuming that the test data set is a “good” representation of what the classification algorithm will see in its applied use. However, in practical applications, there may be situations where certain mistakes/confusions in classification are inconsequential to system operation. In this work, a method for integration of expert-defined allowable confusions into SVM systems is introduced, with an example implementation in a least squares support vector machine (LS-SVM) tested on industrial data, and shown to improve overall performance of a multi-class classification system when an appropriate performance measurement method is formulated.