The Use of Background Knowledge in Decision Tree Induction
Machine Learning
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Logical analysis of numerical data
Mathematical Programming: Series A and B - Special issue: papers from ismp97, the 16th international symposium on mathematical programming, Lausanne EPFL
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
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Robust separation of finite sets via quadratics
Computers and Operations Research
AI Game Programming Wisdom
Support vector machines with different norms: motivation, formulations and results
Pattern Recognition Letters
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Mathematical Programming in Data Mining
Data Mining and Knowledge Discovery
Two-phases Method and Branch and Bound Procedures to Solve the Bi–objective Knapsack Problem
Journal of Global Optimization
Linear Programming Boosting via Column Generation
Machine Learning
On Feature Selection with Measurement Cost and Grouped Features
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Mathematical Programming for Data Mining: Formulations and Challenges
INFORMS Journal on Computing
Optimization methods in massive data sets
Handbook of massive data sets
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
An introduction to variable and feature selection
The Journal of Machine Learning Research
Journal of Artificial Intelligence Research
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
Binarized Support Vector Machines
INFORMS Journal on Computing
Review: Supervised classification and mathematical optimization
Computers and Operations Research
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Support Vector Machine has shown to have good performance in many practical classification settings. In this paper we propose, for multi-group classification, a biobjective optimization model in which we consider not only the generalization ability (modeled through the margin maximization), but also costs associated with the features. This cost is not limited to an economical payment, but can also refer to risk, computational effort, space requirements, etc. We introduce a Biobjective Mixed Integer Problem, for which Pareto optimal solutions are obtained. Those Pareto optimal solutions correspond to different classification rules, among which the user would choose the one yielding the most appropriate compromise between the cost and the expected misclassification rate.