Adaptive Sequential Feature Selection for Pattern Classification

Liliya Avdiyenko, Nils Bertschinger, Juergen Jost

2012

Abstract

Feature selection helps to focus resources on relevant dimensions of input data. Usually, reducing the input dimensionality to the most informative features also simplifies subsequent tasks, such as classification. This is, for instance, important for systems operating in online mode under time constraints. However, when the training data is of limited size, it becomes difficult to define a single small subset of features sufficient for classification of all data samples. In such situations, one should select features in an adaptive manner, i.e. use different feature subsets for every testing sample. Here, we propose a sequential adaptive algorithm that for a given testing sample selects features maximizing the expected information about its class. We provide experimental evidence that especially for small data sets our algorithm outperforms two the most similar information-based static and adaptive feature selectors.

Paper Citation

in Harvard Style

Avdiyenko L., Bertschinger N. and Jost J. (2012). Adaptive Sequential Feature Selection for Pattern Classification . In Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2012) ISBN 978-989-8565-33-4, pages 474-482. DOI: 10.5220/0004146804740482

in Bibtex Style

@conference{ncta12,
author={Liliya Avdiyenko and Nils Bertschinger and Juergen Jost},
title={Adaptive Sequential Feature Selection for Pattern Classification},
booktitle={Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2012)},
year={2012},
pages={474-482},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004146804740482},
isbn={978-989-8565-33-4},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2012)
TI - Adaptive Sequential Feature Selection for Pattern Classification
SN - 978-989-8565-33-4
AU - Avdiyenko L.
AU - Bertschinger N.
AU - Jost J.
PY - 2012
SP - 474
EP - 482
DO - 10.5220/0004146804740482