Enhancing SVM with visualization

Abstract

Understanding the result produced by a data-mining algorithm is as important as the accuracy. Unfortunately, support vector machine (SVM) algorithms provide only the support vectors used as "black box" to efficiently classify the data with a good accuracy. This paper presents a cooperative approach using SVM algorithms and visualization methods to gain insight into a model construction task with SVM algorithms. We show how the user can interactively use cooperative tools to support the construction of SVM models and interpret them. A pre-processing step is also used for dealing with large datasets. The experimental results on Delve, Statlog, UCI and bio-medical datasets show that our cooperative tool is comparable to the automatic LibSVM algorithm, but the user has a better understanding of the obtained model. © Springer-Verlag 2004.