Speeding up hoeffding-based regression trees with options

• Type: Article in International Conference Proceedings Book
• Date: 2011

Authors

Ikonomovska, E (Author) Other; João Gama (Author) FEP; Zenko, B (Author) Other; Dzeroski, S (Author) Other

Conference proceedings International

Title: Proceedings of the 28th International Conference on Machine Learning, ICML 2011

Pages: 537-544

28th International Conference on Machine Learning, ICML 2011

Bellevue, WA, 28 June 2011 through 2 July 2011

Indexing

Scopus - 37 Citations

Other information

Authenticus ID: P-008-0JC

Abstract (EN): Data streams are ubiquitous and have in the last two decades become an important research topic. For their predictive non-parametric analysis, Hoeffding-based trees are often a method of choice, offering a possibility of any-time predictions. However, one of their main problems is the delay in learning progress due to the existence of equally discriminative attributes. Options are a natural way to deal with this problem. Option trees build upon regular trees by adding splitting options in the internal nodes. As such they are known to improve accuracy, stability and reduce ambiguity. In this paper, we present on-line option trees for faster learning on numerical data streams. Our results show that options improve the any-time performance of ordinary on-line regression trees, while preserving the interpretable structure of trees and without significantly increasing the computational complexity of the algorithm. Copyright 2011 by the author(s)/owner(s).

Language: English

Type (Professor's evaluation): Scientific