GOG-MBSHO: multi-strategy fusion binary sea-horse optimizer with Gaussian transfer function for feature selection of cancer gene expression data

Cancer gene expression data has the characteristics of high-dimensional, multi-text and multi-classification. The problem of cancer subtype diagnosis can be solved by selecting the most representative and predictive genes from a large number of gene expression data. Feature selection technology can...

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Vydané v:The Artificial intelligence review Ročník 57; číslo 12; s. 347
Hlavní autori: Wang, Yu-Cai, Song, Hao-Ming, Wang, Jie-Sheng, Song, Yu-Wei, Qi, Yu-Liang, Ma, Xin-Ru
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Dordrecht Springer Netherlands 01.12.2024
Springer
Springer Nature B.V
Predmet:
Accuracy
Algorithms
Artificial Intelligence
Cancer
Classification
Computer Science
Convergence
Data
Datasets
Exploitation
Feature selection
Function
Gene expression
Genes
Genetic aspects
Global local relationship
Horses
Intelligence
Medical diagnosis
Movement
Optimization
Simulation
Strategies
Technology assessment
Transfer functions
Trigonometric functions
Feature selection
Gaussian transfer function
Cancer gene expression
Multi-strategy fusion
Sea-horse optimizer
ISSN:1573-7462, 0269-2821, 1573-7462
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Popis
Shrnutí:Cancer gene expression data has the characteristics of high-dimensional, multi-text and multi-classification. The problem of cancer subtype diagnosis can be solved by selecting the most representative and predictive genes from a large number of gene expression data. Feature selection technology can effectively reduce the dimension of data, which helps analyze the information on cancer gene expression data. A multi-strategy fusion binary sea-horse optimizer based on Gaussian transfer function (GOG-MBSHO) is proposed to solve the feature selection problem of cancer gene expression data. Firstly, the multi-strategy includes golden sine strategy, hippo escape strategy and multiple inertia weight strategies. The sea-horse optimizer with the golden sine strategy does not disrupt the structure of the original algorithm. Embedding the golden sine strategy within the spiral motion of the sea-horse optimizer enhances the movement of the algorithm and improves its global exploration and local exploitation capabilities. The hippo escape strategy is introduced for random selection, which avoids the algorithm from falling into local optima, increases the search diversity, and improves the optimization accuracy of the algorithm. The advantage of multiple inertial weight strategies is that dynamic exploitation and exploration can be carried out to accelerate the convergence speed and improve the performance of the algorithm. Then, the effectiveness of multi-strategy fusion was demonstrated by 15 UCI datasets. The simulation results show that the proposed Gaussian transfer function is better than the commonly used S-type and V-type transfer functions, which can improve the classification accuracy, effectively reduce the number of features, and obtain better fitness value. Finally, comparing with other binary swarm intelligent optimization algorithms on 15 cancer gene expression datasets, it is proved that the proposed GOG1-MBSHO has great advantages in the feature selection of cancer gene expression data.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1573-7462
0269-2821
1573-7462
DOI:10.1007/s10462-024-10954-5