Abstract
Decision rules, which can provide good interpretability and flexibility for data mining tasks, have received very little attention in the stream mining community so far. In this work we introduce a new algorithm to learn rule sets, designed for open-ended data streams. The proposed algorithm is able to continuously learn compact ordered and unordered rule sets. The experimental evaluation shows competitive results in comparison with VFDT and C4.5rules.

Abstract

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).

Abstract

Abstract

Abstract