Binary Grey Wolf Optimizer with Mutation and Adaptive K-nearest Neighbour for Feature Selection in Parkinson’s Disease Diagnosis

Rajalaxmi, R. R.; Mirjalili, Seyedali; Gothai, E.; Natesan, P.

doi:10.1016/j.knosys.2022.108701

Cited by 41 publications

(25 citation statements)

References 52 publications

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“…Rajammal et al developed a binary Grey Wolf Optimizer (BIGWO) algorithm for classifying Parkinson's disease using feature selection [15]. They proposed an adaptive KNN classifier instead of a classical KNN classifier.…”

Section: Related Workmentioning

confidence: 99%

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Multi-objective Harris Hawk Algorithms for the Diagnosis of Parkinson's Disease

Dökeroğlu

2023

Preprint

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This study proposes new binary Harris Hawk Optimization algorithms for diagnosing Parkinson's disease. New exploration and exploitation operators are developed, and a K-Nearest Neighbour classifier that adapts to the given dataset is employed. A parallel version of the algorithm implemented using Message Passing Interfaces is proposed for large problem instances where the fitness evaluation is time-consuming. Comparisons with state-of-the-art genetic, particle swarm, binary bat, cuckoo search, and grey wolf algorithms verified that our proposed algorithms are the best methods in the literature on average in terms of prediction accuracy values. In total, a 32.5% reduction is achieved in the number of features of all datasets. We report new best solutions for the first time in literature. For three datasets out of four, we outperformed state-of-the-art algorithms.

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Section: Related Workmentioning

confidence: 99%

“…We preferred KNN as a classifier in our algorithm because of its high performance in the recent classification studies [15]. KNN is a non-parametric classification and regression technique used for supervised learning [12] [62].…”

Section: K-nearest Neighbours Classifier (Knn)mentioning

confidence: 99%

Multi-objective Harris Hawk Algorithms for the Diagnosis of Parkinson's Disease

Dökeroğlu

2023

Preprint

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“…In order to verify the feasibility and effectiveness of the BGOA-TVG, we adopted five binary meta-heuristic algorithms: BDA [23], BHHO [20], BPSO [11], [12], BGWO [14], BGA [48], BWOA [17], BGBO [46], BGOA-TVG. To make a fair comparison, the population size of the 7 algorithms is 40, and the number of iterations is 100.…”

Section: Parameter Settingsmentioning

confidence: 99%

“…Metaheuristic algorithms have been successfully used to solving complex optimization problems, such as, function optimization [4][5][6], engineering optimization [7][8][9], feature selection problem [10], and so on. The researchers have proposed binary metaheuristic algorithms or improved versions for feature selection such as binary swarm optimization (BPSO) [11], Binary Artificial Bee Colony (BABC) [12], Binary Gravitational Search Algorithm (BGSA) [13], Binary Grey Wolf Optimizer (BGWO) [14], Binary Salp Swarm Algorithm (BSSA) [15], Binary Bat Algorithm (BBA) [16], Binary Whale Optimization Algorithm (BWOA) [17], Binary Spotted Hyena Optimizer (BSHO) [18], Binary Emperor Penguin Optimizer (BEPO) [19], Binary Harris Hawks Optimization (BHHO) [20], Binary Equilibrium Optimizer(BEO) [21], Binary Atom Search Optimization (BASO) [22], Binary Dragonfly Algorithm [23], Binary Jaya Algorithm (BJA) [24], Binary Coronavirus Herd Immunity Optimizer (BCHIO) [25], Binary Butterfly Optimization Algorithm (BBOA) [26], Binary Black Widow Optimization (BBWO) [27], Binary Slime Mould Algorithm(BSMA) [28], Binary Golden Eagle Optimizer (BGEO) [29] and so on. An important step in feature selection problem is mapping continuous space to the binary ones, so the transfer function play a significant role in the process.…”

Section: Introductionmentioning

confidence: 99%

BGOA-TVG: A binary grasshopper optimization algorithm with time-varying Gaussian transfer functions for feature selection

Li,

Luo,

Zhou

2023

Preprint

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Feature selection aims to select crucial features to improve classification accuracy in machine learning and data mining. In this paper, a new binary grasshopper optimization algorithm using time-varying Gaussian transfer functions (BGOA-TVG) is proposed for feature selection. Compared with the traditional S-shaped and V-shaped transfer functions, the proposed Gaussian time-varying transfer functions has the characteristics of fast convergence speed and strong global search capability to convert the continuous search space to the binary one. The BGOA-TVG is tested and compared to S-shaped, V-shaped binary grasshopper optimization algorithm and five state-of-the-art swarm intelligence algorithms in feature selection. The experimental results show that BGOA-TVG has better performance in UCI and DEAP datasets for the feature selection.

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“…Zhou et al [5] proposed a new wrapper feature selection method based on a correlation-guided Genetic Algorithm (GA). Rajalaxmi Ramasamy Rajammal et al [6] developed a wrapper-based Binary Improved Grey Wolf Optimizer (BIGWO) approach for categorizing Parkinson's disease with an optimal set of features. In this research, we adopt the wrapper method considering that the combination of the heuristic algorithm and wrapper method usually obtains better classification performance.…”

Section: Introductionmentioning

confidence: 99%

Binary grey wolf optimizer with a novel population adaptation strategy for feature selection

Wang

et al. 2023

IET Control Theory & Appl

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Feature selection is a fundamental pre‐processing step in machine learning that aims to reduce the dimensionality of a dataset by selecting the most effective features from the original features. This process is regarded as a combinatorial optimization problem, and the grey wolf optimizer (GWO), a novel meta‐heuristic algorithm, has gained popularity in feature selection due to its fast convergence speed and easy implementation. In this paper, an improved binary GWO algorithm incorporating a novel Population Adaptation strategy called PA‐BGWO is proposed. The PA‐BGWO takes into account the characteristics of the feature selection problem and designs three strategies. The proposed strategy includes an adaptive individual update procedure to enhance the exploitation ability and accelerate convergence speed, a head wolf fine‐tuned mechanism to exert the impact on each independent feature of the objective function, and a filter‐based method ReliefF for calculating feature weights with dynamically adjusted mutation probabilities based on the ranking features to effectively escape from local optima. Experimental comparisons with several state‐of‐the‐art feature selection methods on 15 classification problems demonstrate that the proposed approach can select a small feature subset with higher classification accuracy in most cases.

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Binary Grey Wolf Optimizer with Mutation and Adaptive K-nearest Neighbour for Feature Selection in Parkinson’s Disease Diagnosis

Cited by 41 publications

References 52 publications

Multi-objective Harris Hawk Algorithms for the Diagnosis of Parkinson's Disease

Multi-objective Harris Hawk Algorithms for the Diagnosis of Parkinson's Disease

BGOA-TVG: A binary grasshopper optimization algorithm with time-varying Gaussian transfer functions for feature selection

Binary grey wolf optimizer with a novel population adaptation strategy for feature selection

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