Model Selection for Dynamic Processes

Wu, Shaomin and Flach, Peter A. (2002) Model Selection for Dynamic Processes. In: 2nd International Workshop on Integration and Collaboration Aspects of Data Mining, Decision Support and Meta-Learning, 19th August 2002, Helsinki, Finland. (Unpublished) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

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Abstract

In machine learning, ROC (Receiver Operating Characteristic) analysis is widely used in model selection when we consider both class distribution and misclassification costs that must be given at test time. In this paper we consider the case of a dynamic process, such that the class distributions are different in different time periods or states. The main problem is then to decide when to change models according to the different states of the generating process. In this paper we use a control chart to choose models for the process when misclassification costs are considered. Four strategies are considered and model selection approaches are discussed.

Item Type: Conference or workshop item (Paper)
Subjects: H Social Sciences > HA Statistics > HA33 Management Science
Divisions: Faculties > Social Sciences > Kent Business School > Management Science
Depositing User: Shaomin Wu
Date Deposited: 28 Nov 2012 12:13 UTC
Last Modified: 14 Dec 2017 21:56 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/32223 (The current URI for this page, for reference purposes)
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