Adaptive sequential feature selection for pattern classification
Liliya Avdiyenko, Nils Bertschinger, and Jürgen Jost
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Submission date: 05. Jul. 2012
published in: Proceedings of the 4th International Conference on Neural Computation Theory and Applications (NCTA 2012), part of the International Joint Conference of Computational Intelligence (IJCCI), Barcelona, Spain, October 5-7, 2012
[S.l.] : INSTICC, 2012. - P. 474 - 482
Keywords and phrases: Adaptivity, Feature Selection, mutual information, Multivariate Density Estimation, Pattern Recognition
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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.