A wrapper-filter feature selection technique based on ant colony optimization

Ghosh, Manosij; Guha, Ritam; Sarkar, Ram; Abraham, Ajith

doi:10.1007/s00521-019-04171-3

Cited by 146 publications

(61 citation statements)

References 41 publications

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“…For example, [14,19] improved Dragonfly Algorithm (DA) for the FS problem. [20] used Ant Colony Optimization (ACO) for the FS problem. [21] used the Genetic Algorithm(GA) for the FS problem.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Butterfly Optimization Algorithm for Feature Selection

et al. 2020

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Feature selection represents an essential pre-processing step for a wide range of Machine Learning approaches. Datasets typically contain irrelevant features that may negatively affect the classifier performance. A feature selector can reduce the number of these features and maximise the classifier accuracy. This paper proposes a Dynamic Butterfly Optimization Algorithm (DBOA) as an improved variant to Butterfly Optimization Algorithm (BOA) for feature selection problems. BOA represents one of the most recently proposed optimisation algorithms. BOA has demonstrated its ability to solve different types of problems with competitive results compared to other optimisation algorithms. However, the original BOA algorithm has problems when optimising high-dimensional problems. Such issues include stagnation into local optima and lacking solutions diversity during the optimisation process. To alleviate these weaknesses of the original BOA, two significant improvements are introduced in the original BOA: the development of a Local Search Algorithm Based on Mutation (LSAM) operator to avoid local optima problem and the use of LSAM to improve BOA solutions diversity. To demonstrate the efficiency and superiority of the proposed DBOA algorithm, 20 benchmark datasets from the UCI repository are employed. The classification accuracy, the fitness values, the number of selected features, the statistical results, and convergence curves are reported for DBOA and its competing algorithms. These results demonstrate that DBOA significantly outperforms the comparative algorithms on the majority of the used performance metrics. INDEX TERMS Butterfly optimisation algorithm, feature selection, local search algorithm based on mutation.

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Section: Related Workmentioning

confidence: 99%

Dynamic Butterfly Optimization Algorithm for Feature Selection

et al. 2020

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“…These models are known as embedded models [21,22]. The quality of such models depends a lot on how wrapper and filter parts interact with each other which requires an extra level of tuning leading to higher computational complexity.…”

Section: One Of the Most Sought Areas After Dimension Reduction Procementioning

confidence: 99%

BEO: Binary Equilibrium Optimizer Combined with Simulated Annealing for Feature Selection

Ghosh

Guha

Bera

et al. 2020

Preprint

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This work proposed a binary variant of the recently-proposed Equilibrium Optimizer (EO) to solve binary problems. A v-shaped transfer function is used to map continuous values created in EO to binary. To improve the exploitation of the Binary Equilibrium Optimizer (BEO), the Simulated Annealing is used as one of the most popular local search methods. The proposed BEO algorithm is applied to 18 UCI datasets and compared to a wide range of algorithms. The results demonstrate the superiority and merits of EO when solving feature selection problems.

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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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A wrapper-filter feature selection technique based on ant colony optimization

Cited by 146 publications

References 41 publications

Dynamic Butterfly Optimization Algorithm for Feature Selection

Dynamic Butterfly Optimization Algorithm for Feature Selection

BEO: Binary Equilibrium Optimizer Combined with Simulated Annealing for Feature Selection

Image Contrast Enhancement using Selfish Herd Optimizer

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