2012
Authors
Rodrigues, PP; Bifet, A; Krishnaswamy, S; Gama, J;
Publication
Proceedings of the ACM Symposium on Applied Computing
Abstract
2011
Authors
Ikonomovska, E; Gama, J; Zenko, B; Dzeroski, S;
Publication
Proceedings of the 28th International Conference on Machine Learning, ICML 2011
Abstract
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).
2010
Authors
Vatsavai, RR; Omitaomu, OA; Gama, J; Chawla, NV; Gaber, MM; Ganguly, AR;
Publication
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Abstract
2004
Authors
Kubat, M; Gama, J; Utgoff, P;
Publication
Intelligent Data Analysis
Abstract
2009
Authors
Gama, J; Carvalho, A; Rodrigues, PP; Aguilar, J;
Publication
Proceedings of the ACM Symposium on Applied Computing
Abstract
2009
Authors
Qiang, Y; Ronghuai, H; Jian, P; Gama, J; Xiaofeng, M;
Publication
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Abstract
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