Incremental multi-target model trees for data streams

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

Authors

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

Conference proceedings International

Title: Proceedings of the ACM Symposium on Applied Computing

Pages: 988-993

26th Annual ACM Symposium on Applied Computing, SAC 2011

TaiChung, 21 March 2011 through 24 March 2011

Indexing

Scopus - 30 Citations

Other information

Authenticus ID: P-007-YS6

DOI: 10.1145/1982185.1982402

Abstract (EN): As in batch learning, one may identify a class of streaming real-world problems which require the modeling of several targets simultaneously. Due to the dependencies among the targets, simultaneous modeling can be more successful and informative than creating independent models for each target. As a result one may obtain a smaller model able to simultaneously explain the relations between the input attributes and the targets. This problem has not been addressed previously in the streaming setting. We propose an algorithm for inducing multi-target model trees with low computational complexity, based on the principles of predictive clustering trees and probability bounds for supporting splitting decisions. Linear models are computed for each target separately, by incremental training of perceptrons in the leaves of the tree. Experiments are performed on synthetic and real-world datasets. The multi-target regression tree algorithm produces equally accurate and smaller models for simultaneous prediction of all the target attributes, as compared to a set of independent regression trees built separately for each target attribute. When the regression surface is smooth, the linear models computed in the leaves significantly improve the accuracy for all of the targets. © 2011 ACM.

Language: English

Type (Professor's evaluation): Scientific