SVM and Graphical Algorithms: A Cooperative Approach

Authors:
Francois Poulet
Affiliations:
ESIEA - Pôle ECD, France
Venue:
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Year:
2004

Citing 0
Cited 7

Visual Methods for Examining SVM Classifiers

Visual Data Mining
Towards Effective Visual Data Mining with Cooperative Approaches

Visual Data Mining
An Efficient Explanation of Individual Classifications using Game Theory

The Journal of Machine Learning Research
High dimensional visual data classification

VIEW'06 Proceedings of the 1st first visual information expert conference on Pixelization paradigm
A general method for visualizing and explaining black-box regression models

ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part II
Quality of classification explanations with PRBF

Neurocomputing
Efficiently explaining the predictions of a probabilistic radial basis function classification network

Intelligent Data Analysis

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present a cooperative approach using both Support Vector Machine (SVM) algorithms and visualization methods. SVM are widely used today and often give high quality results, but they are used as "black-box" (it is very difficult to explain the obtained results) and cannot treat easily very large datasets. We have developed graphical methods to help the user to evaluate and explain the SVM results. The first method is a graphical representation of the separating frontier quality, it is then linked with other visualization tools to help the user explaining SVM results. The information provided by these graphical methods is also used for SVM parameter tuning, they are then used together with automatic algorithms to deal with very large datasets on standard computers. We present an evaluation of our approach with the UCI and the Kent Ridge Bio-medical data sets.