Binary Dragonfly Algorithm for Feature Selection

Mafarja, Majdi; Eleyan, Derar; Jaber, Iyad; Hammouri, Abdelaziz I.; Mirjalili, Seyedali

doi:10.1109/ictcs.2017.43

Cited by 167 publications

(98 citation statements)

References 28 publications

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“…An S-shaped transfer function is applied on the position of each wolf to estimate the position changing. In [30], the continuous Dragonfly algorithm (DA) [31] was modified to tackle the feature selection problem. This is performed by using the V-shaped transfer function that is applied on the step vector value of each search agent.…”

Section: Related Workmentioning

confidence: 99%

Binary Sine Cosine Algorithms for Feature Selection from Medical Data

Taghian¹,

Nadimi-Shahraki²

2019

ACIJ

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A well-constructed classification model highly depends on input feature subsets from a dataset, which may contain redundant, irrelevant, or noisy features. This challenge can be worse while dealing with medical datasets. The main aim of feature selection as a pre-processing task is to eliminate these features and select the most effective ones. In the literature, metaheuristic algorithms show a successful performance to find optimal feature subsets. In this paper, two binary metaheuristic algorithms named S-shaped binary Sine Cosine Algorithm (SBSCA) and V-shaped binary Sine Cosine Algorithm (VBSCA) are proposed for feature selection from the medical data. In these algorithms, the search space remains continuous, while a binary position vector is generated by two transfer functions S-shaped and V-shaped for each solution. The proposed algorithms are compared with four latest binary optimization algorithms over five medical datasets from the UCI repository. The experimental results confirm that using both bSCA variants enhance the accuracy of classification on these medical datasets compared to four other algorithms.

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Section: Related Workmentioning

confidence: 99%

Binary Sine Cosine Algorithms for Feature Selection from Medical Data

Taghian¹,

Nadimi-Shahraki²

2019

ACIJ

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“…Step 8: If the dragonfly has at least one neighbor, the step vector and the position vector of the dragonfly will be calculated according to Equations (24) and (25). If not, the position vector will be updated by Equation (26).…”

Section: The Basic Process Of Da-cksvmmentioning

confidence: 99%

“…Though some literature [20,21] indicates that combining multiple kernel functions can obtain better performance than a single kernel function, little research has provided an in-depth analysis of the performance of SVM classifier with a combined kernel function. There would therefore seem to be a definite need to systematically study the complex optimization problem in the SVM classifier with a combined kernel.In 2015, Mirjalili proposed a new meta-heuristic algorithm called the dragonfly algorithm (DA) [22], which has already been used to solve different optimization problems, such as feature selection [23,24], the knapsack problem [25], and image processing [26]. Considering that DA has an excellent global search ability and there are few studies on SVM classifier with combined kernels in the field of cancer classification, this paper proposed a novel classification algorithm based on DA and SVM classifier with a combined kernel function (DA-CKSVM) to improve the classification ability for cancer diagnosis.…”

mentioning

confidence: 99%

“…In 2015, Mirjalili proposed a new meta-heuristic algorithm called the dragonfly algorithm (DA) [22], which has already been used to solve different optimization problems, such as feature selection [23,24], the knapsack problem [25], and image processing [26]. Considering that DA has an excellent global search ability and there are few studies on SVM classifier with combined kernels in the field of cancer classification, this paper proposed a novel classification algorithm based on DA and SVM classifier with a combined kernel function (DA-CKSVM) to improve the classification ability for cancer diagnosis.…”

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confidence: 99%

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DA-Based Parameter Optimization of Combined Kernel Support Vector Machine for Cancer Diagnosis

2019

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As is well known, the correct diagnosis for cancer is critical to save patients’ lives. Support vector machine (SVM) has already made an important contribution to the field of cancer classification. However, different kernel function configurations and their parameters will significantly affect the performance of SVM classifier. To improve the classification accuracy of SVM classifier for cancer diagnosis, this paper proposed a novel cancer classification algorithm based on the dragonfly algorithm and SVM with a combined kernel function (DA-CKSVM) which was constructed from a radial basis function (RBF) kernel and a polynomial kernel. Experiments were performed on six cancer data sets from University of California, Irvine (UCI) machine learning repository and two cancer data sets from Cancer Program Legacy Publication Resources to evaluate the validity of the proposed algorithm. Compared with four well-known algorithms: dragonfly algorithm-SVM (DA-SVM), particle swarm optimization-SVM (PSO-SVM), bat algorithm-SVM (BA-SVM), and genetic algorithm-SVM (GA-SVM), the proposed algorithm was able to find the optimal parameters of SVM classifier and achieved better classification accuracy on cancer datasets.

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“…3 Recently, several nature-inspired algorithms have been proposed for wrapper-based feature selection. [15][16][17] Finally, the hybrid approaches are characterized by combining techniques from both filter and wrapper methods, allowing for an iteration between the feature selection process and the learning algorithm. Basically, hybrid approaches use the filter method to minimize the search space and the wrapper method to choose the best reduction.…”

Section: Introductionmentioning

confidence: 99%

An adaptive memetic algorithm for feature selection using proximity graphs

Zaher

Berretta

Noman

et al. 2018

Computational Intelligence

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We propose a multivariate feature selection method that uses proximity graphs for assessing the quality of feature subsets. Initially, a complete graph is built, where nodes are the samples, and edge weights are calculated considering only the selected features. Next, a proximity graph is constructed on the basis of these weights and different fitness functions, calculated over the proximity graph, to evaluate the quality of the selected feature set. We propose an iterative methodology on the basis of a memetic algorithm for exploring the space of possible feature subsets aimed at maximizing a quality score. We designed multiple local search strategies, and we used an adaptive strategy for automatic balancing between the global and local search components of the memetic algorithm. The computational experiments were carried out using four well‐known data sets. We investigate the suitability of three different proximity graphs (minimum spanning tree, k‐nearest neighbors, and relative neighborhood graph) for the proposed approach. The selected features have been evaluated using a total of 49 classification methods from an open‐source data mining and machine learning package (WEKA). The computational results show that the proposed adaptive memetic algorithm can perform better than traditional genetic algorithms in finding more useful feature sets. Finally, we establish the competitiveness of our approach by comparing it with other well‐known feature selection methods.

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Binary Dragonfly Algorithm for Feature Selection

Cited by 167 publications

References 28 publications

Binary Sine Cosine Algorithms for Feature Selection from Medical Data

Binary Sine Cosine Algorithms for Feature Selection from Medical Data

DA-Based Parameter Optimization of Combined Kernel Support Vector Machine for Cancer Diagnosis

An adaptive memetic algorithm for feature selection using proximity graphs

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