Feature Selection for Histopathological Image Classification using levy Flight Salp Swarm Optimizer

Venubabu       Rachapudi; Golagani   Lavanya    Devi

Abstract

Background: An efficient feature selection method for Histopathological image classification plays an important role to eliminate irrelevant and redundant features. Therefore, this paper proposes a new levy flight salp swarm optimizer based feature selection method.

Methods: The proposed levy flight salp swarm optimizer based feature selection method uses the levy flight steps for each follower salp to deviate them from local optima. The best solution returns the relevant and non-redundant features, which are fed to different classifiers for efficient and robust image classification.

Results: The efficiency of the proposed levy flight salp swarm optimizer has been verified on 20 benchmark functions. The anticipated scheme beats the other considered meta-heuristic approaches. Furthermore, the anticipated feature selection method has shown better reduction in SURF features than other considered methods and performed well for histopathological image classification.

Conclusion: This paper proposes an efficient levy flight salp Swarm Optimizer by modifying the step size of follower salp. The proposed modification reduces the chances of sticking into local optima. Furthermore, levy flight salp Swarm Optimizer has been utilized in the selection of optimum features from SURF features for the histopathological image classification. The simulation results validate that proposed method provides optimal values and high classification performance in comparison to other methods.

Keywords: Feature extraction, feature selection, meta-heuristic methods, salp swarm algorithm, histopathological image classification, levy flight.

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DOI https://dx.doi.org/10.2174/2213275912666181210165129	Print ISSN 2213-2759
Publisher Name Bentham Science Publisher	Online ISSN 1874-4796

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Feature Selection for Histopathological Image Classification using levy Flight Salp Swarm Optimizer

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