The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Using neural networks for data mining
Future Generation Computer Systems - Special double issue on data mining
Support vector density estimation
Advances in kernel methods
Combining support vector and mathematical programming methods for classification
Advances in kernel methods
Semiparametric support vector and linear programming machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
Principles of data mining
AI Game Programming Wisdom
Support vector machines with different norms: motivation, formulations and results
Pattern Recognition Letters
Linear Programming Boosting via Column Generation
Machine Learning
Feature Selection via Concave Minimization and Support Vector Machines
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
A Feature Selection Newton Method for Support Vector Machine Classification
Computational Optimization and Applications
Column-generation boosting methods for mixture of kernels
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Rule extraction from linear support vector machines
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Multi-group support vector machines with measurement costs: A biobjective approach
Discrete Applied Mathematics
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Does SVM really scale up to large bag of words feature spaces?
IDA'07 Proceedings of the 7th international conference on Intelligent data analysis
Structural risk minimization over data-dependent hierarchies
IEEE Transactions on Information Theory
An Improved Branch-and-Bound Method for Maximum Monomial Agreement
INFORMS Journal on Computing
Review: Supervised classification and mathematical optimization
Computers and Operations Research
A new approach for manufacturing forecast problems with insufficient data: the case of TFT---LCDs
Journal of Intelligent Manufacturing
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The widely used support vector machine (SVM) method has shown to yield very good results in supervised classification problems. Other methods such as classification trees have become more popular among practitioners than SVM thanks to their interpretability, which is an important issue in data mining. In this work, we propose an SVM-based method that automatically detects the most important predictor variables and the role they play in the classifier. In particular, the proposed method is able to detect those values and intervals that are critical for the classification. The method involves the optimization of a linear programming problem in the spirit of the Lasso method with a large number of decision variables. The numerical experience reported shows that a rather direct use of the standard column generation strategy leads to a classification method that, in terms of classification ability, is competitive against the standard linear SVM and classification trees. Moreover, the proposed method is robust; i.e., it is stable in the presence of outliers and invariant to change of scale or measurement units of the predictor variables. When the complexity of the classifier is an important issue, a wrapper feature selection method is applied, yielding simpler but still competitive classifiers.