Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
Communications of the ACM
The nature of statistical learning theory
The nature of statistical learning theory
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
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Support Vector Machines: Training and Applications
Support Vector Machines: Training and Applications
Properties of Support Vector Machines
Properties of Support Vector Machines
A tutorial on support vector regression
Statistics and Computing
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
YALE: rapid prototyping for complex data mining tasks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
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
Artificial Intelligence in Medicine
Decompositional Rule Extraction from Support Vector Machines by Active Learning
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Fuzzy Systems
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part I
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
Support Vector Regression (SVR) solves regression problems based on the concept of Support Vector Machine (SVM) introduced by Vapnik (1995). The main drawback of these newer techniques is their lack of interpretability. In other words, it is difficult for the human analyst to understand the knowledge learnt by these models during training. The most popular way to overcome this difficulty is to extract if-then rules from SVM and SVR. Rules provide explanation capability to these models and improve the comprehensibility of the system. Over the last decade, different algorithms for extracting rules from SVM have been developed. However rule extraction from SVR is not widely available yet. In this paper a novel hybrid approach for extracting rules from SVR is presented. The proposed hybrid rule extraction procedure has two phases: (1) Obtain the reduced training set in the form of support vectors using SVR (2) Train the machine leaning techniques (with explanation capability) using the reduced training set. Machine learning techniques viz., Classification And Regression Tree (CART), Adaptive Network based Fuzzy Inference System (ANFIS) and Dynamic Evolving Fuzzy Inference System (DENFIS) are used in the phase 2. The proposed hybrid rule extraction procedure is compared to stand-alone CART, ANFIS and DENFIS. Extensive experiments are conducted on five benchmark data sets viz. Auto MPG, Body Fat, Boston Housing, Forest Fires and Pollution, to demonstrate the effectiveness of the proposed approach in generating accurate regression rules. The efficiency of these techniques is measured using Root Mean Squared Error (RMSE). From the results obtained, it is concluded that when the support vectors with the corresponding predicted target values are used, the SVR based hybrids outperform the stand-alone intelligent techniques and also the case when the support vectors with the corresponding actual target values are used.