Linear tree

• Type: Article in International Scientific Journal
• Date: 1999

Authors

João Gama (Author) FEP; Pavel Brazdil (Author) FEP

Journal

Title: Intelligent Data Analysis

Vol. 3

Pages: 1-22

ISSN: 1088-467X

Publisher: IOS PRESS

Indexing

Scopus - 44 Citations

Other information

Authenticus ID: P-007-CAQ

DOI: 10.3233/ida-1999-3102

Abstract (EN): In this paper we present system Ltree for propositional supervised learning. Ltree is able to define decision surfaces both orthogonal and oblique to the axes defined by the attributes of the input space. This is done combining a decision tree with a linear discriminant by means of constructive induction. At each decision node Ltree defines a new instance space by insertion of new attributes that are projections of the examples that fall at this node over the hyper-planes given by a linear discriminant function. This new instance space is propagated down through the tree. Tests based on those new attributes are oblique with respect to the original input space. Ltree is a probabilistic tree in the sense that it outputs a class probability distribution for each query example. The class probability distribution is computed at learning time, taking into account the different class distributions on the path from the root to the actual node. We have carried out experiments on twenty one benchmark datasets and compared our system with other well known decision tree systems (orthogonal and oblique) like C4.5, OC1, LMDT, and CART. On these datasets we have observed that our system has advantages in what concerns accuracy and learning times at statistically significant confidence levels.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 21