A new approach to symbolic classification rule extraction based on SVM

Authors:
Dexian Zhang;Tiejun Yang;Ziqiang Wang;Yanfeng Fan
Affiliations:
School of Information Science and Engineering, Henan University of Technology, Zheng Zhou, P.R.C.;School of Information Science and Engineering, Henan University of Technology, Zheng Zhou, P.R.C.;School of Information Science and Engineering, Henan University of Technology, Zheng Zhou, P.R.C.;Computer College, Northwestern Polytechnical University, Xi'an, P.R.C.
Venue:
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Year:
2006

Citing 7
Cited 0

Extracting Refined Rules from Knowledge-Based Neural Networks

Machine Learning
Accelerating neural network training using weight extrapolations

Neural Networks
Rule-extraction by backpropagation of polyhedra

Neural Networks
NeuroRule: A Connectionist Approach to Data Mining

VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Extracting symbolic rules from trained neural network ensembles

AI Communications - Special issue on Artificial intelligence advances in China
Dimensionality Reduction of Unsupervised Data

ICTAI '97 Proceedings of the 9th International Conference on Tools with Artificial Intelligence
Data dimensionality reduction with application to simplifying RBF network structure and improving classification performance

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Quantified Score

Hi-index	0.01

Visualization

Abstract

There still exist two key problems required to be solved in the classification rule extraction, i.e. how to select attributes and discretize continuous attributes effectively. The lack of efficient heuristic information is the fundamental reason that affects the performance of currently used approaches. In this paper, a new measure for determining the importance level of the attributes based on the trained SVM is proposed, which is suitable for both continuous attributes and discrete attributes. Based on this new measure, a new approach for rule extraction from trained SVM and classification problems with continuous attributes is proposed. The performance of the new approach is demonstrated by several computing cases. The experimental results prove that the approach proposed can improve the validity of the extracted rules remarkably compared with other rule extracting approaches, especially for the complicated classification problems.