A Novel Weak Estimator for Dynamic Systems
Document Type
Conference Proceeding
Publication Date
12-4-2017
Publication Title
IEEE Access
Volume
5
First page number:
27354
Last page number:
27365
Abstract
In this paper, we propose a novel approach for classifying incoming continuous data under a non-stationary environment. A class of estimators termed stochastic learning weak estimators has been generalized to include continuous time sampling and countable state categories. The method is founded on non-stationary Markov chain techniques and is useful in diverse applications, such as consumer behavior analysis, e-mail spam classification, or understanding drug effectiveness. In terms of tracking the true state probabilities, these weak estimators consistently outperform traditional competitors such as maximum likelihood estimates. Only one user defined parameter is necessary and the method is free of subjective ‘‘moving window’’ type algorithms. We have conducted extensive simulations and real data analyses for classification purposes.
Keywords
Stochastic learning weak estimators, Dynamic systems, Continuous time Markov chain, Countable state space, Non-stationarity, Classification, Simulation.
Language
eng
Repository Citation
Bhaduri, M.,
Zhan, J.,
Chiu, C.
(2017).
A Novel Weak Estimator for Dynamic Systems.
IEEE Access, 5
27354-27365.
http://dx.doi.org/10.1109/ACCESS.2017.2771448