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Multiobjective Feature Selection of Microarray Data via Distributed Parallel Algorithms

Cao, B, Zhao, J, Yang, P, Yang, P, Liu, X, Qi, J, Simpson, A, Elhoseny, M, Mehmood, I and Muhammad, K Multiobjective Feature Selection of Microarray Data via Distributed Parallel Algorithms. Future Generation Computer Systems. ISSN 0167-739X (Accepted)

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Many real-world problems are large scale and hence difficult to address. Due to the large number of features in microarray datasets, feature selection and classification are even more challenging. Although there are numerous features, not all features contribute to the classification, and some features are even impeditive. Through feature selection, a feature subset that contains only a small quantity of essential features is generated, which can increase the classification accuracy and significantly reduce the time consumption.
In this paper, we construct a multiobjective feature selection model that simultaneously considers classification error, feature number and feature redundancy. For this model, we propose several distributed parallel algorithms through different encodings and an adaptive strategy. Additionally, to reduce the time consumption, various tactics are employed, including feature number constraint, distributed parallelism and sample-wise parallelism. For a batch of microarray datasets, the proposed algorithms are superior to several state-of-the-art multiobjective evolutionary algorithms in terms of both effectiveness and efficiency.

Item Type: Article
Uncontrolled Keywords: 0805 Distributed Computing, 0806 Information Systems
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Computer Science
Publisher: Elsevier
Date Deposited: 27 Feb 2019 10:16
Last Modified: 27 Feb 2019 10:18
URI: http://researchonline.ljmu.ac.uk/id/eprint/10219

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