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Feature Extraction for Dynamic Integration of Classifiers
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| Venue: | (submitted to) Fundamenta Informaticae |
| Citations: | 1 - 0 self |
BibTeX
@MISC{Pechenizkiy_featureextraction,
author = {Mykola Pechenizkiy and Alexey Tsymbal and Seppo Puuronen and David W. Patterson},
title = {Feature Extraction for Dynamic Integration of Classifiers},
year = {}
}
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Abstract
Recent research has shown the integration of multiple classifiers to be one of the most important directions in machine learning and data mining. In this paper, we present an algorithm for the dynamic integration of classifiers in the space of extracted features (FEDIC). It is based on the technique of dynamic integration, in which local accuracy estimates are calculated for each base classifier of an ensemble, in the neighborhood of a new instance to be processed. Generally, the whole space of original features is used to find the neighborhood of a new instance for local accuracy estimates in dynamic integration. However, when dynamic integration takes place in high dimensions the search for the neighborhood of a new instance is problematic, since the majority of space is empty and neighbors can in fact be located far from each other. Furthermore, when noisy or irrelevant features are present it is likely that also irrelevant neighbors will be associated with a test instance. In this paper, we propose to use feature extraction in order to cope with the curse of dimensionality in the dynamic integration of classifiers. We consider classical principal component analysis and two eigenvector-based class-conditional feature extraction methods that take into account class information. Experimental results show that, on some data sets, the use of FEDIC leads to significantly higher ensemble accuracies than the use of plain dynamic integration in the space of original features.
Keyphrases
dynamic integration feature extraction new instance local accuracy estimate original feature whole space irrelevant feature multiple classifier irrelevant neighbor base classifier classical principal component analysis recent research account class information machine learning data set extracted feature high dimension experimental result test instance ensemble accuracy important direction data mining plain dynamic integration eigenvector-based class-conditional feature extraction method
