Incremental Learning with Multiple Classifier Systems Using Correction Filters for Classification

author: José del Campo-Ávila, University of Malaga
published: Oct. 8, 2007,   recorded: September 2007,   views: 4822


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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.

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Reviews and comments:

Comment1 ArtMan, February 19, 2009 at 3:03 p.m.:

Nada, nada... Este al lado de nuestro Manolo un mindundi.
The oscar goes to... Manolo!

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