NDLI: Discounted expert weighting for concept drift

Content Provider	IEEE Xplore Digital Library
Author	Ditzler, G. Rosen, G. Polikar, R.
Copyright Year	2013
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Rowan Univ., Glassboro, NJ, USA (Polikar, R.) \|\| Dept. of Electr. & Comput. Eng., Drexel Univ., Philadelphia, PA, USA (Ditzler, G.; Rosen, G.)
Abstract	Multiple expert systems (MES) have been widely used in machine learning because of their inherent ability to decrease variance and improve generalization performance by receiving advice from more than one expert. However, a typical MES explicitly assumes that training and testing data are independent and identically distributed (iid), which, unfortunately, is often violated in practice when the probability distribution generating the data changes with time. One of the key aspects of any MES algorithm deployed in such environments is the decision rule used to combine the decisions of the experts. Many MES algorithms choose adaptive weighting schemes that adjust the weights of a classifier based on its loss in recent time, or use an average of the experts probabilities. However, in a stochastic setting where the loss of an expert is uncertain at a future point in time, which combiner method is the most reliable? In this work, we show that non-uniform weighting experts can provide a stable upper bound on loss compared to techniques such as a follow-the-Ieader or uniform methodology. Several well-studied MES approaches are tested on a variety of real-world data sets to support and demonstrate the theory.
Starting Page	61
Ending Page	67
File Size	277259
Page Count	7
File Format	PDF
ISBN	9781467358491
DOI	10.1109/CIDUE.2013.6595773
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-04-16
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Upper bound Multiple expert systems Concept drift Markov processes Probability distribution Data models Nonstationary environments Expert systems
Content Type	Text
Resource Type	Article

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