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Interval Approach for Evolving Granular System Modeling

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Learning in Non-Stationary Environments

Abstract

Physical systems change over time and usually produce considerable amount of nonstationary data. Evolving modeling of time-varying systems requires adaptive and flexible procedures to deal with heterogeneous data. Granular computing provides a rich framework for modeling time-varying systems using nonstationary granular data streams. This work considers interval granular objects to accommodate essential information from data streams and simplify complex real-world problems. We briefly discuss a new class of problems emerging in data stream mining where data may be either singular or granular. Particularly, we emphasize interval data and interval modeling framework. Interval-based evolving modeling (IBeM) approach recursively adapts both parameters and structure of rule-based models. IBeM uses ∪-closure granular structures to approximate functions. In general, approximand functions can be time series, decision boundaries between classes, control, or regression functions. Essentially, IBeM accesses data sequentially and discards previous examples; incoming data may trigger structural adaptation of models. The IBeM learning algorithm evolves and updates rules quickly to track system and environment changes. Experiments using heterogeneous streams of meteorological and financial data are performed to show the usefulness of the IBeM approach in actual scenarios.

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Acknowledgements

The first author acknowledges CAPES, Brazilian Ministry of Education, for his fellowship. The second author thanks the Energy Company of Minas Gerais - CEMIG, Brazil, for grant P&D178. The last author is grateful to CNPq, the Brazilian National Research Council, for grant 304596/2009-4. The authors also thank the climate data sets from the ECA&D and EU-FP6 project Millennium and the economical data set from the Yahoo! Finance. Comments and suggestions of anonymous referees helped to improve the manuscript and are kindly acknowledged.

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Authors and Affiliations

  1. School of Electrical and Computer Engineering, University of Campinas, Sao Paulo, Brazil

    Daniel Leite & Fernando Gomide

  2. Graduate Program in Electrical Engineering, Pontifical Catholic University of Minas Gerais, Belo Horizonte, Brazil

    Pyramo Costa

Authors
  1. Daniel Leite
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  2. Pyramo Costa
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  3. Fernando Gomide
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Correspondence to Daniel Leite .

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Editors and Affiliations

  1. , Départment Informatique et Automatique, Ecole des Mines de Douai, 941, Rue Charles Bourseul, Douai cedex, 59508, France

    Moamar Sayed-Mouchaweh

  2. University of Linz, Weissdornweg 16, Linz, 4232, Austria

    Edwin Lughofer

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Leite, D., Costa, P., Gomide, F. (2012). Interval Approach for Evolving Granular System Modeling. In: Sayed-Mouchaweh, M., Lughofer, E. (eds) Learning in Non-Stationary Environments. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-8020-5_11

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  • DOI: https://doi.org/10.1007/978-1-4419-8020-5_11

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  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4419-8019-9

  • Online ISBN: 978-1-4419-8020-5

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