Feature Selection Using Chaotic Salp Swarm Algorithm for Data Classification

Hegazy, Ah. E.; Makhlouf, Mohamed; El-Tawel, Gh. S.

doi:10.1007/s13369-018-3680-6

Cited by 70 publications

(34 citation statements)

References 29 publications

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“…In addition, to assure fairness comparison for all the algorithms, the maximum iterations for each algorithm was set to 50 iterations, and the population size was set to 10. Further, the experiments were repeated for 30 times; these settings are recommended by [8] and [45]. Therefore, the results were obtained from the average of 30 trials.…”

Section: Parameter Settingmentioning

confidence: 99%

Improved Harris Hawks Optimization Using Elite Opposition-Based Learning and Novel Search Mechanism for Feature Selection

et al. 2020

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The rapid increase in data volume and features dimensionality have a negative influence on machine learning and many other fields, such as decreasing classification accuracy and increasing computational cost. Feature selection technique has a critical role as a preprocessing step in reducing these issues. It works by eliminating the features that may negatively influence the classifiers' performance, such as irrelevant, redundant and less informative features. This paper aims to introduce an improved Harris hawks optimization (IHHO) by utilizing elite oppositebased learning and proposing a new search mechanism. Harris hawks optimization (HHO) is a novel metaheuristic general-purpose algorithm recently introduced to solve continuous search problems. Compared to conventional HHO, the proposed IHHO can avoid trapping in local optima and has an enhanced search mechanism, relying on mutation, mutation neighborhood search, and rollback strategies to raise the search capabilities. Moreover, it improves population diversity, computational accuracy, and accelerates convergence rate. To evaluate the performance of IHHO, we conducted a series of experiments on twenty benchmark datasets collected from the UCI repository and the scikit-feature project. The datasets represent different levels of feature dimensionality, such as low, moderate, and high. Further, four criteria were adopted to determine the superiority of IHHO: classification accuracy, fitness value, number of selected features, and statistical tests. Furthermore, a comparison between IHHO and other well-known algorithms such as Generic algorithm (GA), Grasshopper Optimization Algorithm (GOA), Particle Swarm Optimization (PSO), Ant Lion Optimizer (ALO), Whale Optimization Algorithm (WOA), Butterfly Optimization Algorithm (BOA) and Slime Mould Algorithm (SMA) was performed. The experimental results have confirmed the dominance of IHHO over the other optimization algorithms in different aspects, such as accuracy, fitness value, and feature selection.

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Section: Parameter Settingmentioning

confidence: 99%

Improved Harris Hawks Optimization Using Elite Opposition-Based Learning and Novel Search Mechanism for Feature Selection

et al. 2020

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“…The chaotic maps are sensitive to the initial parameter meta-heuristic algorithms which have randomness parameters. The use of chaotic sequences in CSSA help to escape from the local minima compared to the original SSA [54]- [56]. Table (3) shows the mathematical form of the ten adapted chaotic maps.…”

Section: Volume 4 2016mentioning

confidence: 99%

Breast Cancer Segmentation From Thermal Images Based on Chaotic Salp Swarm Algorithm

et al. 2020

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Breast cancer is one of the most common types of cancer and early detection can significantly decrease the associated mortality rate. Different kinds of segmentation methods were applied to extract regions of interest from breast cancer images that are necessary to improve the classification. In this paper, a segmentation method for breast cancer from thermal images is introduced based on a proposed Chaotic Salp Swarm Algorithm (CSSA). Although the Salp Swarm Algorithm (SSA) shows superiority in singleobjective optimization problems, it suffers from a low convergence rate and local optima stagnation. In the proposed method, a segmentation algorithm is formulated using the quick-shift method for superpixels extraction whose parameters are optimized by CSSA. The quick-shift method generates compact and nearly uniform superpixels by clustering the breast thermal image pixels. CSSA algorithm is developed based on ten chaotic maps to enhance the original SSA convergence rate while accuracy could be improved by controlling the balance between exploration and exploitation. The proposed algorithm is applied to realworld thermal images for the breast area. The results demonstrate that the proposed CSSA algorithm achieves fast convergence for the unimodal benchmark functions and outperforms the original SSA algorithm. Moreover, a dataset from Mastology Research with Infrared Image (DMR-IR) is used to test the performance of the proposed algorithm. In experiments, the proposed optimized segmentation algorithm extracts the breast area from the background accurately where the region of interest is focused on the breast area and removes the unwanted area such as underarms and stomach which intern can enhance the results of cancer detection. Furthermore, the proposed algorithms achieve robustness for the segmentation of different healthy and unhealthy cases images compared to the state-of-the-art methods.

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“…e expression we propose meets this requirement F − 1 S (proposed) (0) � 0, and the excellent performance will be presented in Section 5. [14] and has been used in several aspects including wireless sensor networks [15], feature selection [16][17][18], parameter estimation [19], and clustering [20]. Salps usually exist in the form of a swarm called salp chain in deep oceans.…”

Section: Expression For Approximating Nakagami-m Quantilementioning

confidence: 99%

“…As for followers, we use the third strategy to update their quantum rotation angles. Equations (15) and (16) give the third strategy, we first compute an auxiliary quantum rotation angle…”

Section: Approximation For Nakagami-m Quantile Functionmentioning

confidence: 99%

An Efficient Approximation for Nakagami‐m Quantile Function Based on Generalized Opposition‐Based Quantum Salp Swarm Algorithm

Gao

Hou

Zhang

et al. 2019

Mathematical Problems in Engineering

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With the further research in communication systems, especially in wireless communication systems, a statistical model called Nakagami-mdistribution appears to have better performance than other distributions, including Rice and Rayleigh, in explaining received faded envelopes. Therefore, the Nakagami-mquantile function plays an important role in numerical calculations and theoretical analyses for wireless communication systems. However, it is quite difficult to operate numerical calculations and theoretical analyses because Nakagami-mquantile function has no exact closed-form expression. In order to obtain the closed-form expression that is able to fit the curve of Nakagami-mquantile function as well as possible, we adopt the method of curve fitting in this paper. An efficient expression for approximating the Nakagami-mquantile function is proposed first and then a novel heuristic optimization algorithm—generalized opposition-based quantum salp swarm algorithm (GO-QSSA)—which contains quantum computation, intelligence inspired by salp swarm and generalized opposition-based learning strategy in quantum space, to compute the coefficients of the proposed expression. Meanwhile, we compare GO-QSSA with three swarm intelligence algorithms: artificial bee colony algorithm (ABC), particle swarm optimization algorithm (PSO), and salp swarm algorithm (SSA). The comparing simulation results reveal that GO-QSSA owns faster convergence speed than PSO, ABC, and SSA. Moreover, GO-QSSA is capable of computing more accurately than traditional algorithms. In addition, the simulation results show that compared with existing curve-fitting-based methods, the proposed expression decreases the fitting error by roughly one order of magnitude in most cases and even higher in some cases. Our approximation is proved to be simple and efficient.

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Feature Selection Using Chaotic Salp Swarm Algorithm for Data Classification

Cited by 70 publications

References 29 publications

Improved Harris Hawks Optimization Using Elite Opposition-Based Learning and Novel Search Mechanism for Feature Selection

Improved Harris Hawks Optimization Using Elite Opposition-Based Learning and Novel Search Mechanism for Feature Selection

Breast Cancer Segmentation From Thermal Images Based on Chaotic Salp Swarm Algorithm

An Efficient Approximation for Nakagami‐m Quantile Function Based on Generalized Opposition‐Based Quantum Salp Swarm Algorithm

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