Adapting peepholing to regression trees

• Type: Article in International Conference Proceedings Book
• Date: 2005

Authors

Torgo, L (Author) FEP; Marques, J (Author) Other

Conference proceedings International

Title: Progress in Artificial Intelligence, 12th Portuguese Conference on Artificial Intelligence, EPIA 2005

Pages: 293-303

12th Portuguese Conference on Artificial Intelligence

Covilhã, Portugal, December 5-8

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 0 Citations

Scientific classification

FOS: Natural sciences > Computer and information sciences

Other information

Authenticus ID: P-000-6MN

DOI: 10.1007/11595014_30

Abstract (EN): This paper presents an adaptation of the peepholing method to regression trees. Peepholing was described as a means to overcome the major computational bottleneck of growing classification trees by Catlett [3]. This method involves two major steps: shortlisting and blinkering. The former has the goal of eliminating some continuous variables from consideration when growing the tree, while the second tries to restrict the range of values of the remaining continuous variables that should be considered when searching for the best cut point split. Both are effective means of overcoming the most costly step of growing tree-based models: sorting the values of the continuous variables before selecting their best split. In this work we describe the adaptations that are necessary to use this method within regression trees. The major adaptations involve developing means to obtain biased estimates of the criterion used to select the best split of these models. We present some preliminary experiments that show the effectiveness of our proposal.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 11