Metaheuristics for optimal feature selection in high-dimensional datasets

Segera, Davies; Mbuthia, Mwangi; Nyete, Abraham

doi:10.1016/b978-0-323-91781-0.00013-2

Cited by 2 publications

(6 citation statements)

References 46 publications

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“…2. The subset Φ will thus contain the features with the minimum of redundancy and of noisy as well as the maximum of relevance [1,5,14,47,48].…”

Section: Feature Selection Overviewmentioning

confidence: 99%

“…Thus, an efficient metaheuristic for the resolution of the NP-Hard optimization problems of feature selection, will browse the vertices of the hypercube ℬ 𝑑 by exploration and exploitation phases in a reasonable time in search of a good solution close to the optimal solution. The natureinspired and population-based metaheuristics are the most likely to grant us the benefits of feature selection [1,47].…”

Section: Mathematical Theory Of the Feature Selectionmentioning

confidence: 99%

“…The increase of irrelevant and redundant features composing high-dimensional data complicates the design of systems based on deep learning and favors the curse of dimension [1]. This observation does not spare any scientific field: cybersecurity, medicine, economy, chemistry, physics, etc.…”

Section: Introductionmentioning

confidence: 99%

“…Feature selections are classified into four main methods: filter, wrapper, embedded and hybridized [1,[3][4][5]. Although more computationally expensive, the most efficient method for a deep learning classifier trained by a high-dimensional data is the wrapper method [1] and it is the one that we exploit in this article.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

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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“…2. The subset Φ will thus contain the features with the minimum of redundancy and of noisy as well as the maximum of relevance [1,5,14,47,48].…”

Section: Feature Selection Overviewmentioning

confidence: 99%

Section: Mathematical Theory Of the Feature Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

SASSI,

CHELOUAH

2024

Preprint

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Optimizing the Hybrid Feature Selection in the DNA Microarray for Cancer Diagnosis Using Fuzzy Entropy and the Giza Pyramid Construction Algorithm

Motevalli,

Khalilian,

Bastanfard

2024

Int. J. Comp. Intel. Appl.

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Biotechnological analysis of DNA microarray genes provides valuable insights into the discovery and treatment of diseases such as cancer. It may also be crucial for the prevention and treatment of other genetic diseases. However, due to the large number of features and dimensions in a DNA microarray, the “curse of dimensions” problem is very common. Many machine learning methods require an effective subset of input genes to achieve high accuracy. Unfortunately, extracting features (genes) is an inherently NP-hard problem. Recently, the use of metaheuristics to overcome the NP-hardness of the feature extraction problem has attracted the attention of many researchers. In this paper, we use the combination of fuzzy entropy and Giza Pyramid Construction (GPC) for feature selection. First, redundant features in the microarray dataset are removed using the fuzzy entropy approach. GPC is then used to reduce the execution time. This results in the selection of a near-optimal subset of genes for cancer detection. Dimensionality reduction with GPC followed by classification with Convolutional Neural Network (CNN) creates a synergy to increase efficiency. The proposed method is tested on five well-known cancer patient datasets: leukemia, lymphoma, MLL, ovarian, and SRBCT. The performance of CNN was also measured with four well-known classifiers, including K-nearest neighbor, naïve Bayesian, decision tree, and logistic regression. Our results show that, on average, CNN has the highest accuracy, recall, precision, and F-measure in all datasets.

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Metaheuristics for optimal feature selection in high-dimensional datasets

Cited by 2 publications

References 46 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

Optimizing the Hybrid Feature Selection in the DNA Microarray for Cancer Diagnosis Using Fuzzy Entropy and the Giza Pyramid Construction Algorithm

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