Multi-Population Genetic Algorithm for Multilabel Feature Selection Based on Label Complementary Communication

Park, Jaegyun; Park, Minwoo; Kim, Dae-Won; Lee, Jae-Sung

doi:10.3390/e22080876

Cited by 19 publications

(25 citation statements)

References 41 publications

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“…A population is divided into multiple small sub-populations who evolve with their own evolution operations; every once in a while, sub-populations interact with each other via merging and communication processes to maintain population diversity and avoid premature convergence [46,47]. The general flow of the multi-population optimization method is shown in Figure 4.…”

Section: The Basics Of the Multi-population Optimization Methodsmentioning

confidence: 99%

Multi-Population Parallel Wolf Pack Algorithm for Task Assignment of UAV Swarm

Wang

2021

Applied Sciences

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The effectiveness of the Wolf Pack Algorithm (WPA) in high-dimensional discrete optimization problems has been verified in previous studies; however, it usually takes too long to obtain the best solution. This paper proposes the Multi-Population Parallel Wolf Pack Algorithm (MPPWPA), in which the size of the wolf population is reduced by dividing the population into multiple sub-populations that optimize independently at the same time. Using the approximate average division method, the population is divided into multiple equal mass sub-populations whose better individuals constitute an elite sub-population. Through the elite-mass population distribution, those better individuals are optimized twice by the elite sub-population and mass sub-populations, which can accelerate the convergence. In order to maintain the population diversity, population pretreatment is proposed. The sub-populations migrate according to a constant migration probability and the migration of sub-populations are equivalent to the re-division of the confluent population. Finally, the proposed algorithm is carried out in a synchronous parallel system. Through the simulation experiments on the task assignment of the UAV swarm in three scenarios whose dimensions of solution space are 8, 30 and 150, the MPPWPA is verified as being effective in improving the optimization performance.

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Section: The Basics Of the Multi-population Optimization Methodsmentioning

confidence: 99%

Multi-Population Parallel Wolf Pack Algorithm for Task Assignment of UAV Swarm

Wang

2021

Applied Sciences

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“…We evaluate our proposed feature selection algorithm RQBSO and compare it with three major groups of feature selection methods, including two filter methods, namely Max-Relevance and Min-Redundancy (mRMR) [58] and ReliefF [59]. Three wrapper methods, namely GA [39], BSO [44], and recursive feature elimination (RFE) [60]. Our method is also compared with two leading embedded methods, namely LASSO [30], and Ridge regression [61].…”

Section: Comparison With Other Feature Selection Algorithmsmentioning

confidence: 99%

“…To address the above issues, various works are proposed to solve feature selection problems using metaheuristics [35]. Most of them use genetic algorithms (GA) [36][37][38][39]. Meta-heuristic algorithms based on swarm intelligence are also applied to feature selection, such as ant colony optimization (ACO) [40,41], particle swarm optimization (PSO) [42,43], and bee swarm optimization (BSO) [44,45].…”

Section: Introductionmentioning

confidence: 99%

Framework for feature selection of predicting the diagnosis and prognosis of necrotizing enterocolitis

Song

Yao

et al. 2022

PLoS ONE

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Neonatal necrotizing enterocolitis (NEC) occurs worldwide and is a major source of neonatal morbidity and mortality. Researchers have developed many methods for predicting NEC diagnosis and prognosis. However, most people use statistical methods to select features, which may ignore the correlation between features. In addition, because they consider a small dimension of characteristics, they neglect some laboratory parameters such as white blood cell count, lymphocyte percentage, and mean platelet volume, which could be potentially influential factors affecting the diagnosis and prognosis of NEC. To address these issues, we include more perinatal, clinical, and laboratory information, including anemia—red blood cell transfusion and feeding strategies, and propose a ridge regression and Q-learning strategy based bee swarm optimization (RQBSO) metaheuristic algorithm for predicting NEC diagnosis and prognosis. Finally, a linear support vector machine (linear SVM), which specializes in classifying high-dimensional features, is used as a classifier. In the NEC diagnostic prediction experiment, the area under the receiver operating characteristic curve (AUROC) of dataset 1 (feeding intolerance + NEC) reaches 94.23%. In the NEC prognostic prediction experiment, the AUROC of dataset 2 (medical NEC + surgical NEC) reaches 91.88%. Additionally, the classification accuracy of the RQBSO algorithm on the NEC dataset is higher than the other feature selection algorithms. Thus, the proposed approach has the potential to identify predictors that contribute to the diagnosis of NEC and stratification of disease severity in a clinical setting.

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“…The optimization algorithms verified their efficiency for improving classification accuracy and reducing the selected features. Samples of these recent implementations are PSO [22], BOA [23], SSA [8], ALO [24], WOA [21,25], GOA [26], and GA [27]. Despite the unique construction of each optimization algorithm, there are some shared characteristics: initializing a random population (solutions) as the opening process, evaluating the solutions on each iteration based on the fitness function, updating the solution, and determining the best solution based on a termination term.…”

Section: Related Workmentioning

confidence: 99%

Improved Equilibrium Optimization Algorithm Using Elite Opposition-Based Learning and New Local Search Strategy for Feature Selection in Medical Datasets

et al. 2021

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The rapid growth in biomedical datasets has generated high dimensionality features that negatively impact machine learning classifiers. In machine learning, feature selection (FS) is an essential process for selecting the most significant features and reducing redundant and irrelevant features. In this study, an equilibrium optimization algorithm (EOA) is used to minimize the selected features from high-dimensional medical datasets. EOA is a novel metaheuristic physics-based algorithm and newly proposed to deal with unimodal, multi-modal, and engineering problems. EOA is considered as one of the most powerful, fast, and best performing population-based optimization algorithms. However, EOA suffers from local optima and population diversity when dealing with high dimensionality features, such as in biomedical datasets. In order to overcome these limitations and adapt EOA to solve feature selection problems, a novel metaheuristic optimizer, the so-called improved equilibrium optimization algorithm (IEOA), is proposed. Two main improvements are included in the IEOA: The first improvement is applying elite opposite-based learning (EOBL) to improve population diversity. The second improvement is integrating three novel local search strategies to prevent it from becoming stuck in local optima. The local search strategies applied to enhance local search capabilities depend on three approaches: mutation search, mutation–neighborhood search, and a backup strategy. The IEOA has enhanced the population diversity, classification accuracy, and selected features, and increased the convergence speed rate. To evaluate the performance of IEOA, we conducted experiments on 21 biomedical benchmark datasets gathered from the UCI repository. Four standard metrics were used to test and evaluate IEOA’s performance: the number of selected features, classification accuracy, fitness value, and p-value statistical test. Moreover, the proposed IEOA was compared with the original EOA and other well-known optimization algorithms. Based on the experimental results, IEOA confirmed its better performance in comparison to the original EOA and the other optimization algorithms, for the majority of the used datasets.

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Multi-Population Genetic Algorithm for Multilabel Feature Selection Based on Label Complementary Communication

Cited by 19 publications

References 41 publications

Multi-Population Parallel Wolf Pack Algorithm for Task Assignment of UAV Swarm

Multi-Population Parallel Wolf Pack Algorithm for Task Assignment of UAV Swarm

Framework for feature selection of predicting the diagnosis and prognosis of necrotizing enterocolitis

Improved Equilibrium Optimization Algorithm Using Elite Opposition-Based Learning and New Local Search Strategy for Feature Selection in Medical Datasets

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