Please use this identifier to cite or link to this item: https://ruomo.lib.uom.gr/handle/7000/671
Title: A Reduced Variable Neighborhood Search Approach for Feature Selection in Cancer Classification
Authors: Pentelas, Angelos
Sifaleras, Angelo
Koloniari, Georgia
Editors: Benmansour, Rachid
Sifaleras, Angelo
Mladenović, Nenad
Type: Conference Paper
Subjects: FRASCATI::Natural sciences::Mathematics::Applied Mathematics
FRASCATI::Natural sciences::Computer and information sciences
FRASCATI::Medical and Health sciences::Basic medicine::Human Genetics
Keywords: Reduced Variable Neighborhood Search
Feature selection
Cancer classification
Issue Date: 8-Apr-2020
Publisher: Springer
Volume: 12010
First Page: 1
Last Page: 16
Volume Title: Variable Neighborhood Search
Part of Series: Lecture Notes in Computer Science
Part of Series: Lecture Notes in Computer Science
Abstract: In this work we propose a Reduced Variable Neighborhood Search (RVNS) algorithm, to handle the gene selection problem in cancer classification. RVNS is utilized as the search method and gene subsets obtained are evaluated by three learning algorithms, namely support vector machine, k-nearest neighbors, and random forest. Experiments are conducted on five publicly available cancer related datasets, all characterized by a small sample size to dimensionality ratio. Since RVNS seeks gene subsets that yield accurate predictions for all three aforementioned classifiers, the obtained results can be considered more reliable. To the best of our knowledge, the proposed methodology is innovative due to the fact that, it combines the Recursive Feature Elimination (RFE) heuristic with a RVNS algorithm. Despite the large size of the problem instances, the suggested feature selection scheme converges within reasonably short time, when compared to similar methods. Results indicate high performance for RVNS that, is further improved when the RFE method is applied as a pre-processing step.
URI: https://doi.org/10.1007/978-3-030-44932-2_1
https://ruomo.lib.uom.gr/handle/7000/671
ISBN: 978-3-030-44931-5
978-3-030-44932-2
ISSN: 0302-9743
1611-3349
Other Identifiers: 10.1007/978-3-030-44932-2_1
Appears in Collections:Department of Applied Informatics

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