Communications of the ACM
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
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Knowledge Discovery in Databases
Knowledge Discovery in Databases
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Detecting Concept Drift with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International 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 Machines with Symbolic Interpretation
SBRN '02 Proceedings of the VII Brazilian Symposium on Neural Networks (SBRN'02)
Support Vector Machines Based on a Semantic Kernel for Text Categorization
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 5 - Volume 5
Web taxonomy integration using support vector machines
Proceedings of the 13th international conference on World Wide Web
Rule extraction: using neural networks or for neural networks?
Journal of Computer Science and Technology
Hierarchical document categorization with support vector machines
Proceedings of the thirteenth ACM international conference on Information and knowledge management
A review of explanation methods for heuristic expert systems
The Knowledge Engineering Review
Rule extraction from linear support vector machines
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
A support vector method for multivariate performance measures
ICML '05 Proceedings of the 22nd international conference on Machine learning
Fuzzy Rule Extraction from Support Vector Machines
HIS '05 Proceedings of the Fifth International Conference on Hybrid Intelligent Systems
Extracting Trees from Trained SVM Models using a TREPAN Based Approach
HIS '05 Proceedings of the Fifth International Conference on Hybrid Intelligent Systems
Rule-Based Learning Systems for Support Vector Machines
Neural Processing Letters
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Rule Extraction from Support Vector Machines: A Sequential Covering Approach
IEEE Transactions on Knowledge and Data Engineering
Twin Support Vector Machines for Pattern Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Artificial Intelligence in Medicine
Decompositional Rule Extraction from Support Vector Machines by Active Learning
IEEE Transactions on Knowledge and Data Engineering
Semantic role labeling via tree kernel joint inference
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Semi-supervised learning for semantic parsing using support vector machines
NAACL-Short '07 Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers
An evaluation of the usefulness of case-based explanation
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
Rule extraction from trained support vector machines
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
IEEE Transactions on Neural Networks
Content-based audio classification and retrieval by support vector machines
IEEE Transactions on Neural Networks
Nonlinear mappings in problem solving and their PSO-based development
Information Sciences: an International Journal
Comparative analysis of data mining methods for bankruptcy prediction
Decision Support Systems
Credit risk assessment and decision making by a fusion approach
Knowledge-Based Systems
Rule extraction from support vector machines based on consistent region covering reduction
Knowledge-Based Systems
Integration of semi-fuzzy SVDD and CC-Rule method for supplier selection
Expert Systems with Applications: An International Journal
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Over the last decade, support vector machine classifiers (SVMs) have demonstrated superior generalization performance to many other classification techniques in a variety of application areas. However, SVMs have an inability to provide an explanation, or comprehensible justification, for the solutions they reach. It has been shown that the 'black-box' nature of techniques like artificial neural networks (ANNs) is one of the main obstacles impeding their practical application. Therefore, techniques for rule extraction from ANNs, and recently from SVMs, were introduced to ameliorate this problem and aid in the explanation of their classification decisions. In this paper, we conduct a formal review of the area of rule extraction from SVMs. The review provides a historical perspective for this area of research and conceptually groups and analyzes the various techniques. In particular, we propose two alternative groupings; the first is based on the SVM (model) components utilized for rule extraction, while the second is based on the rule extraction approach. The aim is to provide a better understanding of the topic in addition to summarizing the main features of individual algorithms. The analysis is then followed by a comparative evaluation of the algorithms' salient features and relative performance as measured by a number of metrics. It is concluded that there is no one algorithm that can be favored in general. However, methods that are kernel independent, produce the most comprehensible rule set and have the highest fidelity to the SVM should be preferred. In addition, a specific method can be preferred if the context of the requirements of a specific application, so that appropriate tradeoffs may be made. The paper concludes by highlighting potential research directions such as the need for rule extraction methods in the case of SVM incremental and active learning and other application domains, where special types of SVMs are utilized.