Transductive learning algorithms for nonstationary environments

Gregory Ditzler, Gail Rosen, Robi Polikar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

Many traditional supervised machine learning approaches, either on-line or batch based, assume that data are sampled from a fixed yet unknown source distribution. Most incremental learning algorithms also make the same assumption, even though new data are presented over periods of time. Yet, many real-world problems are characterized by data whose distribution change over time, which implies that a classifier may no longer be reliable on future data, a problem commonly referred to as concept drift or learning in nonstationary environments. The issue is further complicated when the problem requires prediction from data obtained at a future time step, for which the labels are not yet available. In this work, we present a transductive learning methodology that uses probabilistic models to aid in computing ensemble classifier voting weights. Assuming the drift is limited in nature, the proposed approach exploits a probabilistic estimate to determine the class responsibility of components in a Gaussian mixture model (GMM), generated from labeled and unlabeled data. A general error bound is provided based on the ensemble decision, the probabilistic estimate of the GMM, and the true labeling function, which, unfortunately is never actually known.

Original languageEnglish (US)
Title of host publication2012 International Joint Conference on Neural Networks, IJCNN 2012
DOIs
StatePublished - Aug 22 2012
Externally publishedYes
Event2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Part of the 2012 IEEE World Congress on Computational Intelligence, WCCI 2012 - Brisbane, QLD, Australia
Duration: Jun 10 2012Jun 15 2012

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Conference

Conference2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Part of the 2012 IEEE World Congress on Computational Intelligence, WCCI 2012
CountryAustralia
CityBrisbane, QLD
Period6/10/126/15/12

Keywords

  • Gaussian mixture models
  • concept drift
  • multiple classifier systems
  • unlabeled data

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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  • Cite this

    Ditzler, G., Rosen, G., & Polikar, R. (2012). Transductive learning algorithms for nonstationary environments. In 2012 International Joint Conference on Neural Networks, IJCNN 2012 [6252494] (Proceedings of the International Joint Conference on Neural Networks). https://doi.org/10.1109/IJCNN.2012.6252494