Oblique Multicategory Decision Trees Using Nonlinear Programming
INFORMS Journal on Computing · 2005 · Volume 17 · Issue 1
Abstract
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.
Keywords
artificial intelligence, programming, nonlinear, applications, machine learning, decision trees
Enable JavaScript for citation tools and additional paper interactions.