A Hybrid Improved Dragonfly Algorithm for Feature Selection

Cui, Xueting; Li, Ying; Fan, Jiahao; Wang, Tan; Zheng, Yang

doi:10.1109/access.2020.3012838

Cited by 34 publications

(24 citation statements)

References 52 publications

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“…For the DLBCL dataset, the work mentioned in [ 14 , 26 , 30 ] attained an accuracy of 100% but the number of features selected by the authors were greater than the number of features selected by our proposed method. They utilized 16, 6, and 8 features, respectively, whereas the proposed method selected only four features as the optimal feature set.…”

Section: Resultsmentioning

confidence: 86%

“…It can be seen from Table 6 that all the other works achieved less accuracy while utilizing more features than our proposed method. The authors of [ 11 , 14 , 15 ] selected a very large number of features (236, 135, and 169 features, respectively) as an optimal feature set and achieved 82.96%, 74.77%, and 88.72% accuracies, respectively. Authors of the work [ 12 ] succeeded in selecting a comparatively lesser number of features (30 features), but the accuracy obtained was 85.58%.…”

Section: Resultsmentioning

confidence: 99%

“…They achieved an accuracy of 99.02% with 135 selected features. Cui et al [ 14 ] proposed a feature selection technique that combines the benefit of both minimum redundancy maximum relevance (mRMR) method and improved dragonfly algorithm (IDA), naming it hybrid improved dragonfly algorithm (HIDA). Firstly, they used mRMR to discard irrelevant features and then they introduced dynamic swarming factors and quantum local optimum and global optimum to amplify the exploitation capability of IDA.…”

Section: Literature Surveymentioning

confidence: 99%

“…This method achieved the highest accuracy of 100% with six selected features for the DLBCL dataset. Authors of the work [ 14 ] implemented the proposed hybrid improved dragonfly algorithm (HIDA) method on DLBCL dataset and obtained 100% accuracy with an average count of 16 features.…”

Section: Literature Surveymentioning

confidence: 99%

See 3 more Smart Citations

A Tri-Stage Wrapper-Filter Feature Selection Framework for Disease Classification

Mandal

Singh

Ijaz

et al. 2021

Sensors

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In machine learning and data science, feature selection is considered as a crucial step of data preprocessing. When we directly apply the raw data for classification or clustering purposes, sometimes we observe that the learning algorithms do not perform well. One possible reason for this is the presence of redundant, noisy, and non-informative features or attributes in the datasets. Hence, feature selection methods are used to identify the subset of relevant features that can maximize the model performance. Moreover, due to reduction in feature dimension, both training time and storage required by the model can be reduced as well. In this paper, we present a tri-stage wrapper-filter-based feature selection framework for the purpose of medical report-based disease detection. In the first stage, an ensemble was formed by four filter methods—Mutual Information, ReliefF, Chi Square, and Xvariance—and then each feature from the union set was assessed by three classification algorithms—support vector machine, naïve Bayes, and k-nearest neighbors—and an average accuracy was calculated. The features with higher accuracy were selected to obtain a preliminary subset of optimal features. In the second stage, Pearson correlation was used to discard highly correlated features. In these two stages, XGBoost classification algorithm was applied to obtain the most contributing features that, in turn, provide the best optimal subset. Then, in the final stage, we fed the obtained feature subset to a meta-heuristic algorithm, called whale optimization algorithm, in order to further reduce the feature set and to achieve higher accuracy. We evaluated the proposed feature selection framework on four publicly available disease datasets taken from the UCI machine learning repository, namely, arrhythmia, leukemia, DLBCL, and prostate cancer. Our obtained results confirm that the proposed method can perform better than many state-of-the-art methods and can detect important features as well. Less features ensure less medical tests for correct diagnosis, thus saving both time and cost.

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Section: Resultsmentioning

confidence: 86%

Section: Resultsmentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

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A Tri-Stage Wrapper-Filter Feature Selection Framework for Disease Classification

Mandal

Singh

Ijaz

et al. 2021

Sensors

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show abstract

“…The BDA algorithm was applied to different learning algorithm such as Naik et al [194] used BDA with radial basis neural network function and selected the features from microarray gene data. Several other binary versions of DA have been proposed to solve features selection problem that can be found in [195]- [198].…”

Section: B Swarm Intelligence Based Algorithmsmentioning

confidence: 99%

Metaheuristic Algorithms on Feature Selection: A Survey of One Decade of Research (2009-2019)

et al. 2021

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Feature selection is a critical and prominent task in machine learning. To reduce the dimension of the feature set while maintaining the accuracy of the performance is the main aim of the feature selection problem. Various methods have been developed to classify the datasets. However, metaheuristic algorithms have achieved great attention in solving numerous optimization problem. Therefore, this paper presents an extensive literature review on solving feature selection problem using metaheuristic algorithms which are developed in the ten years (2009-2019). Further, metaheuristic algorithms have been classified into four categories based on their behaviour. Moreover, a categorical list of more than a hundred metaheuristic algorithms is presented. To solve the feature selection problem, only binary variants of metaheuristic algorithms have been reviewed and corresponding to their categories, a detailed description of them explained. The metaheuristic algorithms in solving feature selection problem are given with their binary classification, name of the classifier used, datasets and the evaluation metrics. After reviewing the papers, challenges and issues are also identified in obtaining the best feature subset using different metaheuristic algorithms. Finally, some research gaps are also highlighted for the researchers who want to pursue their research in developing or modifying metaheuristic algorithms for classification. For an application, a case study is presented in which datasets are adopted from the UCI repository and numerous metaheuristic algorithms are employed to obtain the optimal feature subset.

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On the Pivotal Role of Artificial Intelligence Toward the Evolution of Smart Grids

Refaat¹,

Abu‐Rub²

2021

Smart Grid and Enabling Technologies

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A Hybrid Improved Dragonfly Algorithm for Feature Selection

Cited by 34 publications

References 52 publications

A Tri-Stage Wrapper-Filter Feature Selection Framework for Disease Classification

A Tri-Stage Wrapper-Filter Feature Selection Framework for Disease Classification

Metaheuristic Algorithms on Feature Selection: A Survey of One Decade of Research (2009-2019)

On the Pivotal Role of Artificial Intelligence Toward the Evolution of Smart Grids

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