Solving Feature Selection Problems Built on Population‐based Metaheuristic Algorithms

Sassi, Mohamed

doi:10.1002/9781119902881.ch3

Cited by 3 publications

(3 citation statements)

References 13 publications

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“…If the feature selection is an NP-Hard problem to solve, the final solution Φ to this problem will provide benefits commensurate with the complexity of it [1,3,34,40,[49][50][51][52]. We count ten main benefits.…”

Section: The Benefits Of the Feature Selection Processmentioning

confidence: 99%

“…The later required the modification of the equations for calculating the position of the agents. One also could adopt a binary discretization which will limit the number of possible states to 0 or 1 [51].…”

Section: Hybridization Of Hho-eas For Solving the Wrapper Fs-mopmentioning

confidence: 99%

“…We therefore performed a high-level and sequential hybridization integrated in the HHO-EAS metaheuristic [69]. Our hybridization is composed of modules 𝑀1 integrating transfer functions and 𝑀2 integrating their associated logic rules [51]. These two modules are suitably positioned in HHO-EAS so that at each iteration the continuous positions 𝑋 𝑖 in ℝ 𝑑 of an agent 𝑖 are converted into a binary position 𝐵 𝑖 in ℬ 𝑑 [70].…”

Section: Hybridization Of Hho-eas For Solving the Wrapper Fs-mopmentioning

confidence: 99%

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BHHO-EAS metaheuristic applied to the NP-Hard wrapper feature selection multi-objective optimization problem

SASSI,

CHELOUAH

2024

Preprint

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Faced with the increase in high-dimensional Big Data creating more volume and complexity, the feature selection process became an essential phase in the preprocessing workflow upstream of the design of systems based on deep learning. This paper is a concrete and first application of the new metaheuristic Harris Hawk Optimization Encirclement-Attack-Synergy (HHO-EAS) in solving the NP-Hard wrapper feature selection multi-objective optimization problem. This problem combines two contradictory objectives: maximizing the accuracy of a classifier while minimizing the number of the most relevant and non-redundant selected features. To do this we hybridized HHO-EAS to create the new metaheuristic Binary HHO-EAS (BHHO-EAS). We combined HHO-EAS to the sixteen transfer functions most used in the literature structured in a balanced way among the four main categories including S-Shaped, V-Shaped, Q-Shaped and U-Shaped. This wide range of transfer function allows us to analyze the evolution of BHHO-EAS’s skills according to the assigned transfer function and to determine which of them offer the best performances. We applied wrapper feature selection to the well-known NSL-KDD dataset with the deep learning Multi Layer Perceptron (MLP) classifier. We put BHHO-EAS in competition with three other well-known population based binary metaheuristics, BPSO, BBA and BHHO. The analysis of the experimental results, compared to the three other binary metaheuristics, demonstrated that BHHO-EAS obtained the best performance on 100% of the transfer functions. This is more particularly highlighted by the U-Shaped transfer functions, which give an acceptable compromise for the two objectives of the problem with an average accuracy of 96,4% and an average size of selected features of 20.

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Section: The Benefits Of the Feature Selection Processmentioning

confidence: 99%

Section: Hybridization Of Hho-eas For Solving the Wrapper Fs-mopmentioning

confidence: 99%

Section: Hybridization Of Hho-eas For Solving the Wrapper Fs-mopmentioning

confidence: 99%

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BHHO-EAS metaheuristic applied to the NP-Hard wrapper feature selection multi-objective optimization problem

SASSI,

CHELOUAH

2024

Preprint

Self Cite

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High Blood Pressure Classification Using Meta-heuristic Based Data-Centric Hybrid Machine Learning Model

Nimmala

Rani

Rao

2023

Communications in Computer and Information Science

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A modified binary version of aphid–ant mutualism for feature selection: a COVID-19 case study

Eslami

Yazdani

Mirzaei

et al. 2023

Journal of Computational Design and Engineering

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The speedy development of intelligent technologies and gadgets has led to a drastic increment of dimensions within the datasets in recent years. Dimension reduction algorithms such as feature selection methods are crucial to resolving this obstacle. Currently, metaheuristic algorithms have been extensively used in feature selection tasks due to their acceptable computational cost and performance. In this article, a binary-modified version of Aphid-Ant Mutualism (AAM) called BAAM is introduced to solve the feature selection problems. Like AAM, in BAAM, the intensification and diversification mechanisms are modeled via the intercommunication of aphids with other colonies’ members, including aphids and ants. However, unlike AAM, the number of colonies’ members can change in each iteration based on the attraction power of their leaders. Moreover, the second and third-best individuals can take the place of the ringleader and lead the pioneer colony. Also, to maintain the population diversity, prevent premature convergence, and facilitate information sharing between individuals of colonies including aphids and ants, a random crossover operator is utilized in BAAM. The proposed BAAM is compared with five other feature selection algorithms using several evaluation metrics. Twelve medical and nine non-medical benchmark datasets with different numbers of features, instances, and classes from the University of California, Irvine (UCI) and Arizona state university repositories are considered for all the experiments. Moreover, a coronavirus disease (COVID-19) dataset is used to validate the effectiveness of the BAAM in real-world applications. Based on the acquired outcomes, the proposed BAAM outperformed other comparative methods in terms of classification accuracy using various classifiers, including KNN, KELM, and multi-class SVM, choosing the most informative features, the best and mean fitness values and convergence speed in most cases. As an instance, in the COVID-19 dataset, BAAM achieved 96.53% average accuracy and selected the most informative feature subset.

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Solving Feature Selection Problems Built on Population‐based Metaheuristic Algorithms

Cited by 3 publications

References 13 publications

BHHO-EAS metaheuristic applied to the NP-Hard wrapper feature selection multi-objective optimization problem

BHHO-EAS metaheuristic applied to the NP-Hard wrapper feature selection multi-objective optimization problem

High Blood Pressure Classification Using Meta-heuristic Based Data-Centric Hybrid Machine Learning Model

A modified binary version of aphid–ant mutualism for feature selection: a COVID-19 case study

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