Binary Genetic Swarm Optimization: A Combination of GA and PSO for Feature Selection

Ghosh, Manosij; Guha, Ritam; Alam, Imran; Lohariwal, Priyank; Jalan, Devesh; Sarkar, Ram

doi:10.1515/jisys-2019-0062

Cited by 46 publications

(27 citation statements)

References 43 publications

(45 reference statements)

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“…Based on physical techniques, Henry gas solubility optimization (HGSO) belongs to the third class of physical algorithm, which selects meaningful features with k-NN and SVM to enhance the classification accuracy in [62].…”

Section: Literature Reviewmentioning

confidence: 99%

An Efficient Marine Predators Algorithm for Feature Selection

et al. 2021

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Feature Selection (F.S.) reduces the number of features by removing unnecessary, redundant, and noisy information while keeping a relatively decent classification accuracy. F.S. can be considered an optimization problem. As the problem is challenging and there are many local solutions, stochastic optimization algorithms may be beneficial .This paper proposes a novel approach to dimension reduction in feature selection. As a seminal attempt, this work uses binary variants of the recent Marine Predators Algorithm (MPA) to select the optimal feature subset to improve classification accuracy. MPA is a new and novel nature-inspired metaheuristic. This research proposes an algorithm that is a hybridization between MPA and k-Nearest Neighbors (k-NN) called MPA-KNN. k-Nearest Neighbors (k-NN) is used to evaluate the selected features on medical datasets with feature sizes ranging from tiny to massive. The proposed methods are evaluated on 18 well-known UCI medical dataset benchmarks and compared with eight well-regarded metaheuristic wrapper-based approaches. The core exploratory and exploitative processes are adapted in MPA to select the optimal and meaningful features for achieving the most accurate classification. The results show that the proposed MPA-KNN approach had a remarkable capability to select the optimal and significant features. It performed better than the well-established metaheuristic algorithms we tested. The algorithms we used for comparison are Grey Wolf Optimizer (GWO), MothFlame Optimization Algorithm (MFO), Sine Cosine Algorithm (SCA), Whale Optimization Algorithm (WOA), Slap Swarm Algorithm (SSA), Butterfly Optimization Algorithm (BFO), and Harris Hawks Optimization (HHO). This paper is the first work that implements MPA for Feature Selection problems. The results ensure that the proposed MPA-KNN approach has a remarkable capability to select the optimal and significant features and performed better than several metaheuristic algorithms. MPA-KNN achieves the best averages accuracy, Sensitivity, and Specificity rates of all datasets.

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Section: Literature Reviewmentioning

confidence: 99%

An Efficient Marine Predators Algorithm for Feature Selection

et al. 2021

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“…Optimization algorithms have been applied successfully for FS in many applications such as data mining [27] using Particle Swarm Optimization, pattern recognition [28] using Binary Genetic Swarm Optimization, Medical applications [5] using Crow Search Optimization, and image analysis [29] using Genetic Algorithm Optimization, image processing [30], [31], [32] using Optimized Deep Neural Network, and there are many more. Nowadays, FS is an essential step to preprocess high-dimensional datasets.…”

Section: Related Workmentioning

confidence: 99%

An Improved Harris Hawks Optimization Algorithm With Simulated Annealing for Feature Selection in the Medical Field

et al. 2020

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Harris Hawks Optimization (HHO) algorithm is a new metaheuristic algorithm, inspired by the cooperative behavior and chasing style of Harris' Hawks in nature called surprise pounce. HHO demonstrated promising results compared to other optimization methods. However, HHO suffers from local optima and population diversity drawbacks. To overcome these limitations and adapt it to solve feature selection problems, a novel metaheuristic optimizer, namely Chaotic Harris Hawks Optimization (CHHO), is proposed. Two main improvements are suggested to the standard HHO algorithm. The first improvement is to apply the chaotic maps at the initialization phase of HHO to enhance the population diversity in the search space. The second improvement is to use the Simulated Annealing (SA) algorithm to the current best solution to improve HHO exploitation. To validate the performance of the proposed algorithm, CHHO was applied on 14 medical benchmark datasets from the UCI machine learning repository. The proposed CHHO was compared with the original HHO and some famous and recent metaheuristics algorithms, containing Grasshopper Optimization Algorithm (GOA), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Butterfly Optimization Algorithm (BOA), and Ant Lion Optimizer (ALO). The used evaluation metrics include the number of selected features, classification accuracy, fitness values, Wilcoxon's statistical test (P-value), and convergence curve. Based on the achieved results, CHHO confirms its superiority over the standard HHO algorithm and the other optimization algorithms on the majority of the medical datasets.

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“…As there is no perfect solution in ICE, our aim is to find an optimal solution within a reasonable time duration. With most of the other optimizing algorithms like Genetic Algorithm [15], PSO [16], Ant Colony Optimization [17] etc., there is no forcing criteria. So, it may happen that the algorithms keep running even without any significant improvement.…”

Section: Application Of Sho In Icementioning

confidence: 99%

Image Contrast Enhancement using Selfish Herd Optimizer

Guha¹,

Alam²,

Bera³

et al. 2020

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Contrast enhancement is an important pre-processing task in any Image Analysis (IA) system. In this paper, we formulate the image contrast enhancement problem as an optimization problem where the goal is to optimize the pixel intensity values of an input image to obtain a contrast enhanced version of the same. This optimization task is executed by suitably customizing a nature-inspired optimization algorithm called Selfish Herd Optimizer (SHO). The optimization problem is solved using two different solution representations: pixel wise optimization (SHO(direct)) and transformation function based optimization (SHO(transformation)). Moreover, an ablation study is performed to select the most appropriate parameters which can be used in tness measure for this optimization problem. On experimenting over the popular Kodak image dataset, it has been observed that the proposed methods outperform many existing methods published recently. Further comparisons indicate that the direct approach performs better than its transformation counterpart. This paper further investigates the robustness of SHO(direct) approach by applying it to enhance the degraded document images of H-DIBCO 2018.

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Binary Genetic Swarm Optimization: A Combination of GA and PSO for Feature Selection

Cited by 46 publications

References 43 publications

An Efficient Marine Predators Algorithm for Feature Selection

An Efficient Marine Predators Algorithm for Feature Selection

An Improved Harris Hawks Optimization Algorithm With Simulated Annealing for Feature Selection in the Medical Field

Image Contrast Enhancement using Selfish Herd Optimizer

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