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Partial maximum correlation information: A new feature selection method for microarray data classification
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DOI：10.1016/j.neucom.2018.09.084.png)
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摘要
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Feature (gene) selection of microarray data is a very important and challenging task. This paper proposes a new feature selection method, partial maximum correlation information (PMCI), for microarray data classification. The proposed method extracts several orthogonal components from the feature space to evaluate the importance of each feature. In PMCI method, the component extraction is based on the correlation between feature space and class coding space. Meanwhile, this paper also provides a new generic class-encoding scheme for multi-class problems. This type of encoding scheme can ensure the smooth implementation for PMCI and has a clear geometric meaning. To validate the performance of the proposed method, this paper selects six state-of-the-art algorithms for comparison based on ten widely used microarray benchmark datasets for experiment. The experimental results show that PMCI is an effective feature selection method with good performance and lower time complexity. (C) 2018 Elsevier B.V. All rights reserved.
Keyword:
Feature selection
Microarray data
SIMPIS
Partial maximum correlation information (PMCI)
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