C4.5: programs for machine learning
C4.5: programs for machine learning
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Machine Learning
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Rule extraction from linear support vector machines
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Rule extraction from trained support vector machines
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
| Hi-index | 0.00 |
Sigmoid kernel is widely applied in neural networks for classification tasks. SVM classifier, which is applied with sigmoid kernel, has excellent classification accuracy. However, as sigmoid kernel has complicated structure, it is generally difficult for human expert to interpret and understand how the sigmoid kernel makes its classification decision. As decision rule classifier is understandable to human expert, in this paper, we present our InterSIG algorithm, which mines decision rules from the classification hyper-plane which is constructed by SVM with sigmoid kernel. InterSIG expands sigmoid kernel into its Maclaurin series, and then mines classification rules which make great contribution to classification from the classification hyper-plane. Experiment results show that InterSIG classifier is more understandable to human experts without jeopardizing the accuracy than the original SVM with sigmoid kernel. Furthermore, compared with 3 association classifiers, CMAR, CBA, CPAR and C4.5, a decision tree classifier, InterSIG classifier is very encouraging over the 9 datasets.