NDLI: Predicting Future Decision Trees from Evolving Data

Content Provider	IEEE Xplore Digital Library
Author	Bottcher, M. Spott, M. Kruse, R.
Copyright Year	2008
Description	Author affiliation: Intell. Syst. Res. Centre, Ipswich (Spott, M.) \|\| Fac. of Comput. Sci., Univ. of Magdeburg, Magdeburg (Bottcher, M.; Kruse, R.)
Abstract	Recognizing and analyzing change is an important human virtue because it enables us to anticipate future scenarios and thus allows us to act pro-actively. One approach to understand change within a domain is to analyze how models and patterns evolve. Knowing how a model changes over time is suggesting to ask: Can we use this knowledge to learn a model in anticipation, such that it better reflects the near-future characteristics of an evolving domain? In this paper we provide an answer to this question by presenting an algorithm which predicts future decision trees based on a model of change. In particular, this algorithm encompasses a novel approach to change mining which is based on analyzing the changes of the decisions made during model learning. The proposed approach can also be applied to other types of classifiers and thus provides a basis for future research. We present our first experimental results which show that anticipated decision trees have the potential to outperform trees learned on the most recent data.
Starting Page	33
Ending Page	42
File Size	336762
Page Count	10
File Format	PDF
ISBN	9780769535029
ISSN	15504786
DOI	10.1109/ICDM.2008.90
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-15
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Decision trees Data mining Predictive models Pattern analysis Computer science Humans Data warehouses Intelligent systems Prediction algorithms Algorithm design and analysis Evolving Data Decision Trees Change Mining
Content Type	Text
Resource Type	Article

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