Oblique Multicategory Decision Trees Using Nonlinear Programming | Zendy

W. Nick Street | Zendy

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Oblique Multicategory Decision Trees Using Nonlinear Programming

Author(s) -

W. Nick Street

Publication year - 2005

Publication title -

informs journal on computing

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.403

H-Index - 80

eISSN - 1526-5528

pISSN - 1091-9856

DOI - 10.1287/ijoc.1030.0047

Subject(s) - decision tree , generalization , oblique case , incremental decision tree , nonlinear programming , computer science , mathematics , nonlinear system , machine learning , alternating decision tree , mathematical optimization , data mining , artificial intelligence , decision tree learning , algorithm , mathematical analysis , linguistics , philosophy , physics , quantum mechanics

informs ® doi 10.1287/ijoc.1030.0047 © 2005 INFORMS 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

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