Introduction to the theory of neural computation
Introduction to the theory of neural computation
On the induction of decision trees for multiple concept learning
On the induction of decision trees for multiple concept learning
C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
Smoothing methods for convex inequalities and linear complementarity problems
Mathematical Programming: Series A and B
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Machine Learning
Feature Selection Via Mathematical Programming
INFORMS Journal on Computing
Mathematical Programming for Data Mining: Formulations and Challenges
INFORMS Journal on Computing
Pareto-optimality of oblique decision trees from evolutionary algorithms
Journal of Global Optimization
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Induction of decision trees is a popular and effective method for solving classification problems in data-mining applications. This paper presents a new algorithm for multi-category decision tree induction based on nonlinear programming. This algorithm, termed OC-SEP (Oblique Category SEParation), combines the advantages of several other methods and shows improved generalization performance on a collection of real-world data sets.