Symbolic and Neural Learning Algorithms: An Experimental Comparison
Machine Learning
Bilinear separation of two sets in n-space
Computational Optimization and Applications
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Advanced Scout: Data Mining and Knowledge Discovery in NBA Data
Data Mining and Knowledge Discovery
The Surgical Separation of Sets
Journal of Global Optimization
Analysis of Bounds for Multilinear Functions
Journal of Global Optimization
An Implementation of Logical Analysis of Data
IEEE Transactions on Knowledge and Data Engineering
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
A system for induction of oblique decision trees
Journal of Artificial Intelligence Research
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This paper presents a mixed 0 - 1 integer and linear programming (MILP) model for separation of data via a finite number of nonlinear and nonconvex discriminant functions. The MILP model concurrently optimizes the parameters of the user-provided individual discriminant functions and implements a decision boundary for an optimal separation of data under analysis. The MILP model is extensively tested on six well-studied datasets in data mining research. The comparison of numerical results by the MILP-based classification of data with those produced by the multisurface method and the support vector machine in these experiments and the best from the literature illustrates the efficacy and the usefulness of the new MILP-based classification of data for supervised learning.