A Nearest Hyperrectangle Learning Method
Machine Learning
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Unifying instance-based and rule-based induction
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
Intelligent Data Analysis: An Introduction
Intelligent Data Analysis: An Introduction
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
K-SVCR. A Multi-class Support Vector Machine
ECML '00 Proceedings of the 11th European Conference on Machine Learning
Applications of Support Vector Machines for Pattern Recognition: A Survey
SVM '02 Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
Rule extraction from linear support vector machines
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Fuzzy Rule Extraction from Support Vector Machines
HIS '05 Proceedings of the Fifth International Conference on Hybrid Intelligent Systems
SVM Soft Margin Classifiers: Linear Programming versus Quadratic Programming
Neural Computation
Artificial Intelligence in Medicine
Expert Systems with Applications: An International Journal
On Minimal Rule Sets for Almost All Binary Information Systems
Fundamenta Informaticae - Half a Century of Inspirational Research: Honoring the Scientific Influence of Antoni Mazurkiewicz
Neighborhood rough set based heterogeneous feature subset selection
Information Sciences: an International Journal
Decompositional Rule Extraction from Support Vector Machines by Active Learning
IEEE Transactions on Knowledge and Data Engineering
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
Rule extraction from support vector machines: A review
Neurocomputing
A support vector machine-based model for detecting top management fraud
Knowledge-Based Systems
Rule learning for classification based on neighborhood covering reduction
Information Sciences: an International Journal
ANN vs. SVM: Which one performs better in classification of MCCs in mammogram imaging
Knowledge-Based Systems
Rule extraction from trained support vector machines
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Jmax-pruning: A facility for the information theoretic pruning of modular classification rules
Knowledge-Based Systems
Distributed customer behavior prediction using multiplex data: A collaborative MK-SVM approach
Knowledge-Based Systems
Integration of semi-fuzzy SVDD and CC-Rule method for supplier selection
Expert Systems with Applications: An International Journal
A method for extracting rules from spatial data based on rough fuzzy sets
Knowledge-Based Systems
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Due to good performance in classification and regression, support vector machines have attracted much attention and become one of the most popular learning machines in last decade. As a black box, the support vector machine is difficult for users' understanding and explanation. In many application domains including medical diagnosis or credit scoring, understandability and interpretability are very important for the practicability of the learned models. To improve the comprehensibility of SVMs, we propose a rule extraction technique from support vector machines via analyzing the distribution of samples. We define the consistent region of samples in terms of classification boundary, and form a consistent region covering of the sample space. Then a covering reduction algorithm is developed for extracting compact representation of classes, thus a minimal set of decision rules is derived. Experiment analysis shows that the extracted models perform well in comparison with decision tree algorithms and other support vector machine rule extraction methods.