Firefly Optimization Algorithm for Feature Selection

Emary, Eid; Zawbaa, Hossam M.; Ghany, Kareem Kamal A.; Hassanien, Aboul Ella; Pârv, Bazil

doi:10.1145/2801081.2801091

Cited by 72 publications

(30 citation statements)

References 16 publications

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“…Taradeh et al [27] propose another gravitational search-based algorithm for feature selection. Emary et al [28] use the firefly algorithm (FA) to select features from several datasets. In [29], the authors propose to use an ant colony optimization (ACO) with support vector machine (SVM) strategy for wrapper feature selection in face recognition.…”

Section: Related Workmentioning

confidence: 99%

Bio-Inspired Feature Selection: An Improved Binary Particle Swarm Optimization Approach

Sun

et al. 2020

IEEE Access

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Feature selection is an effective approach to reduce the number of features of data, which enhances the performance of classification in machine learning. In this paper, we formulate a joint feature selection problem to reduce the number of the selected features while enhancing the accuracy. An improved binary particle swarm optimization (IBPSO) algorithm is proposed to solve the formulated problem. IBPSO introduces a local search factor based on Lévy flight, a global search factor based on weighting inertia coefficient, a population diversity improvement factor based on mutation mechanism and a binary mechanism to improve the performance of conventional PSO and to make it suitable for the binary feature selection problems. Experiments based on 16 classical datasets are selected to test the effectiveness of the proposed IBPSO algorithm, and the results demonstrate that IBPSO has better performance than some other comparison algorithms.

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Section: Related Workmentioning

confidence: 99%

Bio-Inspired Feature Selection: An Improved Binary Particle Swarm Optimization Approach

Sun

et al. 2020

IEEE Access

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“…The FA is a recently developed nature-inspired metaheuristic which was inspired by the light flashing pattern of fireflies [20], [45] There are three general rules guiding the fireflies in the FA:…”

Section: Firegly Algorithmmentioning

confidence: 99%

A classification model on tumor cancer disease based mutual information and firefly algorithm

Jabar¹

2019

PEN

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Cancer is a globally recognized cause of death. A proper cancer analysis demands the classification of several types of tumor. Investigations into microarray gene expressions seem to be a successful platform for revising genetic diseases. Although the standard machine learning (ML) approaches have been efficient in the realization of significant genes and in the classification of new types of cancer cases, their medical and logical application has faced several drawbacks such as DNA microarray data analysis limitation, which includes an incredible number of features and the relatively small size of an instance. To achieve a reasonable and efficient DNA microarray dataset information, there is a need to extend the level of interpretability and forecast approach while maintaining a great level of precision. In this work, a novel way of cancer classification based on based gene expression profiles is presented. This method is a combination of both Firefly algorithm and Mutual Information Method. First, the features are used to select the features before using the Firefly algorithm for feature reduction. Finally, the Support Vector Machine is used to classify cancer into types. The performance of the proposed system was evaluated by using it to classify datasets from colon cancer; the results of the evaluation were compared with some recent approaches.

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“…The researchers achieved high classification rates with few numbers of features in 9 of the 11 for used data. Emary, E. and et al [9] suggested a binary firefly optimization (BFO) algorithm. The proposed method was used in features selection and proves advance over BPSO in various valuation indicators [9].…”

mentioning

confidence: 99%

“…Emary, E. and et al [9] suggested a binary firefly optimization (BFO) algorithm. The proposed method was used in features selection and proves advance over BPSO in various valuation indicators [9]. Mirjalili and Lewis [31] proposed The Whale Optimization Algorithm (WOA), WOA is a modern meta-heuristic, which is inspired by the behavior of humpback whales.…”

mentioning

confidence: 99%

Improving whale optimization algorithm for feature selection with a time-varying transfer function

Kahya¹,

Altamir²,

Algamal³

2021

NACO

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Feature selection is a valuable tool in supervised machine learning research fields, such as pattern recognition or classification problems. Feature selection used to eliminate irrelevant and noise features that adversely affect results. Swarm algorithms are usually used in feature selection problem; these algorithms need transfer functions that change search space from continuous to the discrete. However, transfer functions are the backbone of all binary swarm algorithms. Transfer functions in the current formula cannot provide binary swarm algorithms with a fit balance between exploration and exploitation stages. In this work, a feature selection approach based on the binary whale optimization algorithm with different kinds of updating techniques for the time-varying transfer functions is proposed. To evaluate the performance of the proposed method, three of each chemical and biological binary datasets are used. The results proved that BWOA-TV2 has consistency in feature selection and it gives rise to the high accuracy of the classification with more congruent in the convergence. It worth mentioning that the proposed method is proved advance in performance over competitor optimization algorithms, such as particle swarm optimization (PSO) and firefly optimization (FO) that commonly used in this field.

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Firefly Optimization Algorithm for Feature Selection

Cited by 72 publications

References 16 publications

Bio-Inspired Feature Selection: An Improved Binary Particle Swarm Optimization Approach

Bio-Inspired Feature Selection: An Improved Binary Particle Swarm Optimization Approach

A classification model on tumor cancer disease based mutual information and firefly algorithm

Improving whale optimization algorithm for feature selection with a time-varying transfer function

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