Viz-SVM
Summary. 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.
Keywords: Classification, Support vector machines, Visual data mining,
Information visualization.
