Abstract
Classification is a quite relevant task within data mining area. This task is not trivial and some difficulties can arise depending on the nature of the problem. Multiple classifier systems have been used to construct ensembles of base classifiers in order to solve or alleviate some of those problems. One of the most current problems that is being studied in recent years is how to learn when the datasets are too large or when new information can arrive at any time. In that case, incremental learning is an approach that can be used. Some works have used multiple classifier system to learn in an incremental way and the results are very promising. The aim of this paper is to propose a method for improving the classification (or prediction) accuracy reached by multiple classifier systems in this context.
This work has been partially supported by the FPI program and the MOISES-TA project, number TIN2005-08832-C03, of the MEC, Spain.
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del Campo-Ávila, J., Ramos-Jiménez, G., Morales-Bueno, R. (2007). Incremental Learning with Multiple Classifier Systems Using Correction Filters for Classification. In: R. Berthold, M., Shawe-Taylor, J., Lavrač, N. (eds) Advances in Intelligent Data Analysis VII. IDA 2007. Lecture Notes in Computer Science, vol 4723. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74825-0_10
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DOI: https://doi.org/10.1007/978-3-540-74825-0_10
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