Memory based cuckoo search algorithm for feature selection of gene expression dataset

Alzaqebah, Malek; Briki, Khaoula; Alrefai, Nashat; Brini, Sami; Jawarneh, Sana; Alsmadi, Mutasem K.; Mohammad, Rami Mustafa A.; Almarashdeh, Ibrahim; AlGhamdi, Fahad; Aldhafferi, Nahier; Alqahtani, Abdullah

doi:10.1016/j.imu.2021.100572

Cited by 37 publications

(17 citation statements)

References 27 publications

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“…Experimental results based on different values of parameters show the CS algorithm's final parameter settings as presented in Table 2. In the original CS algorithm, parameters pa, , and  initialized firstly based on [56]. The findings of this study have been implemented using a personal computer with the specifications: Intel i5-2.30 GHz Processor and RAM of 8.0 GB.…”

Section: Resultsmentioning

confidence: 99%

Cuckoo algorithm with great deluge local-search for feature selection problems

Alsmadi

Alzaqebah

Jawarneh

et al. 2022

IJECE

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<p class="Abstract"><span lang="EN-US">Feature selection problem is concerned with searching in a dataset for a set of features aiming to reduce the training time and enhance the accuracy of a classification method. Therefore, feature selection algorithms are proposed to choose important features from large and complex datasets. The cuckoo search (CS) algorithm is a type of natural-inspired optimization algorithms and is widely implemented to find the optimum solution for a specified problem. In this work, the cuckoo search algorithm is hybridized with a local search algorithm to find a satisfactory solution for the problem of feature selection. The great deluge (GD) algorithm is an iterative search procedure, that can accept some worse moves to find better solutions for the problem, also to increase the exploitation ability of CS. The comparison is also provided to examine the performance of the proposed method and the original CS algorithm. As result, using the UCI datasets the proposed algorithm outperforms the original algorithm and produces comparable results compared with some of the results from the literature.</span></p>

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Section: Resultsmentioning

confidence: 99%

Cuckoo algorithm with great deluge local-search for feature selection problems

Alsmadi

Alzaqebah

Jawarneh

et al. 2022

IJECE

Self Cite

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“…In [ 43 ], a hybrid algorithm is proposed using simulated annealing (SA) and the Rao algorithm (RA) for selecting the optimal gene subset and classifying cancer. Reference [ 44 ] proposes the Cuckoo search method as a feature selection algorithm, guided by the memory-based mechanism to save the most informative features that are identified by the best solutions. In [ 45 ], Lu et al introduce a novel hybrid FS method that combines Mutual Information Maximization (MIM) and the Adaptive Genetic Algorithm (AGA), named MIMAGA-Selection.…”

Section: Methodsmentioning

confidence: 99%

EGFAFS: A Novel Feature Selection Algorithm Based on Explosion Gravitation Field Algorithm

Huang

Wang

et al. 2022

Entropy

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Feature selection (FS) is a vital step in data mining and machine learning, especially for analyzing the data in high-dimensional feature space. Gene expression data usually consist of a few samples characterized by high-dimensional feature space. As a result, they are not suitable to be processed by simple methods, such as the filter-based method. In this study, we propose a novel feature selection algorithm based on the Explosion Gravitation Field Algorithm, called EGFAFS. To reduce the dimensions of the feature space to acceptable dimensions, we constructed a recommended feature pool by a series of Random Forests based on the Gini index. Furthermore, by paying more attention to the features in the recommended feature pool, we can find the best subset more efficiently. To verify the performance of EGFAFS for FS, we tested EGFAFS on eight gene expression datasets compared with four heuristic-based FS methods (GA, PSO, SA, and DE) and four other FS methods (Boruta, HSICLasso, DNN-FS, and EGSG). The results show that EGFAFS has better performance for FS on gene expression data in terms of evaluation metrics, having more than the other eight FS algorithms. The genes selected by EGFAGS play an essential role in the differential co-expression network and some biological functions further demonstrate the success of EGFAFS for solving FS problems on gene expression data.

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“…As an extension to the literatures of the same authors of this manuscript, a different methodology is attempting to showcase the variations in the analysis and methodology for the same dataset used in these literatures. Moreover, the impacts the exploration and exploitation capability is balanced with avoidance of over fitting problems in the proposed methodology, where traditional literatures [23][24][25][26][27][28][29][30][31][32][33][34] showed its superiority performance in comparison to the traditional methodologies.…”

Section: Related Workmentioning

confidence: 99%

Feature selection in high‐dimensional microarray cancer datasets using an improved equilibrium optimization approach

Balakrishnan

Dhanalakshmi

2022

Concurrency and Computation

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Optimal feature selection of a high-dimensional micro-array datasets has gained a significant importance in medical applications for early detection and prevention of disease. Traditional Optimal feature selection percolates through a population-based meta-heuristic optimization technique, a Machine Learning classifier and traditional wrapper method for transforming the original feature set into a better feature set.These techniques require a number of iterations for the convergence of random solutions to the global optimum with high-dimensionality issues such as over-fitting, memory constraints, computational costs, and low accuracy. In this article, an efficient equilibrium optimization technique is proposed for an optimized feature selection that increases the diversity of the population in the search space through Random Opposition based learning and classify the best features using a 10-fold cross-validation-based wrapper method. The proposed method is tested with six standard micro-array datasets and compared with the conventional algorithms such as Marine Predators Algorithm, Harris Hawks Optimization, Whale Optimization Algorithm, and conventional Equilibrium Optimization. From the statistical results using the standard metrics, it is interpreted that the proposed method converges to the global minimum in a few iterations through optimized feature selection, fitness value and higher classification accuracy. This proves its efficacy in exploring and finding a better solution as compared to the counterpart algorithms. In addition to complexity analysis, these results indicate a global optimum solution, an effective representation of least amount of data-high dimensionality reduction and an avoidance of over-fitting problems.

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Memory based cuckoo search algorithm for feature selection of gene expression dataset

Cited by 37 publications

References 27 publications

Cuckoo algorithm with great deluge local-search for feature selection problems

Cuckoo algorithm with great deluge local-search for feature selection problems

EGFAFS: A Novel Feature Selection Algorithm Based on Explosion Gravitation Field Algorithm

Feature selection in high‐dimensional microarray cancer datasets using an improved equilibrium optimization approach

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