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A binary clonal flower pollination algorithm for feature selection
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DOI：10.1016/j.patrec.2016.03.014.png)
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Feature selection problem has been detected essentially in the last years. It is a step that is considered the prerequisite of the classification step. For the feature selection problem, the goal is to find out the most important subset of features that represent the original features in a certain domain. The selected features are used in optimization of a certain fitness function, so the feature selection problem can be seen as an optimization problem. This paper presents a new hybrid algorithm that combines Clonal Selection Algorithm (CSA) with Flower Pollination Algorithm (FPA) to compose Binary Clonal Flower Pollination Algorithm (BCFA) to solve the feature selection problem. The accuracy of the Optimum-Path Forest (OPF) classifier is used as an objective function. The experiments were implemented on three public datasets and demonstrated that the proposed hybrid algorithm achieved remarkable results in comparison with other well-known algorithms such as Binary Cuckoo Search Algorithm (BCSA), Binary Bat Algorithm (BBA), Binary Differential Evolution Algorithm (BDEA) and Binary Flower Pollination Algorithm (BFPA). (C) 2016 Elsevier B.V. All rights reserved.
Keyword:
Feature selection
Clonal Selection Algorithm
Flower Pollination Algorithm
Optimum Path Forest
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