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

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 cri...

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Bibliographic Details
Published in:IEEE/ACM transactions on computational biology and bioinformatics 2019-11, Vol.16 (6), p.1802-1815
Main Authors: Brankovic, Aida, Hosseini, Marjan, Piroddi, Luigi
Format: Article
Language:English
Subjects:
Algorithms
classification
Complexity theory
Computational modeling
Computer applications
Correlation
Datasets
Dependence
distance correlation
DNA chips
DNA microarrays
Evaluation
Feature extraction
feature selection
model selection
Optimization
randomized methods
Redundancy
Task analysis
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Summary: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.
ISSN:1545-5963
1557-9964
DOI:10.1109/TCBB.2018.2833482