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A Distributed Feature Selection Algorithm Based on Distance Correlation with an Application to Microarrays by Aida Brankovic; Marjan Hosseini; Luigi Piroddi is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

What is A Distributed Feature Selection Algorithm Based on Distance Correlation with an Application to Microarrays about?

DNA microarray datasets are characterized by a large number of features with very few samples, which is a typical cause of overfitting and poor generalization in the classification task. Here, we introduce a novel feature selection (FS) approach which employs the distance correlation (dCor) as a criterion for evaluating the dependence of the class on a given feature subset. The dCor index provides a reliable dependence measure among random vectors of arbitrary dimension, without any assumption on their distribution. Moreover, it is sensitive to the presence of redundant terms. The proposed FS method is based on a probabilistic representation of the feature subset model, which is progressively refined by a repeated process of model extraction and evaluation. A key element of the approach is a distributed optimization scheme based on a vertical partitioning of the dataset, which alleviates the negative effects of its unbalanced dimensions. The proposed method has been tested on several microarray datasets, resulting in quite compact and accurate models obtained at a reasonable computational cost.

Who reads A Distributed Feature Selection Algorithm Based on Distance Correlation with an Application to Microarrays?

Author

Aida Brankovic; Marjan Hosseini; Luigi Piroddi

Publisher

IEEE; Institute of Electrical and Electronics Engineers; IEEE Computer Society; Institute of Electrical and Electronics Engineers (IEEE) (ISSN 1545-5963)

Published

2018

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)