Feature Selection of Grey Wolf Optimizer Based on Quantum Computing and Uncertain Symmetry Rough Set

Zhao, Guobao; Wang, Haiying; Jia, Deli; Wang, Quanbin

doi:10.3390/sym11121470

Cited by 8 publications

(4 citation statements)

References 34 publications

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“…This algorithm [ 85 ] is inspired by the gray wolf's hunting policies and the leadership role. Gray wolves divide the population into four levels: alpha, beta, delta, and omega.…”

Section: Nature-inspired Algorithms For Feature Selectionmentioning

confidence: 99%

See 1 more Smart Citation

Metaheuristics based COVID-19 detection using medical images: A review

Riaz¹,

Bashir²,

Younas³

2022

Computers in Biology and Medicine

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“…This algorithm [ 85 ] is inspired by the gray wolf's hunting policies and the leadership role. Gray wolves divide the population into four levels: alpha, beta, delta, and omega.…”

Section: Nature-inspired Algorithms For Feature Selectionmentioning

confidence: 99%

“…The searching task is done by following the location of alpha, beta, and delta. This algorithm [ 85 ] has been used for feature selection in COVID-19 detection.…”

Section: Nature-inspired Algorithms For Feature Selectionmentioning

confidence: 99%

Metaheuristics based COVID-19 detection using medical images: A review

Riaz¹,

Bashir²,

Younas³

2022

Computers in Biology and Medicine

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“…Compare with Other Methods. We compare Hu's [19] neighborhood rough set algorithm based on heuristic information, FASRSR [13] and QCGWORS [20] based on swarm intelligence optimization algorithm. The experiment results of the best attribute subset by the four algorithms are shown in Table 4, and the classification accuracy is shown in Table 5.…”

Section: Experimental Performance Testmentioning

confidence: 99%

Neighborhood Rough Set Reduction with Improved Mayfly Optimization Algorithm

Shi

Zhou

Sang

et al. 2021

Proceedings of 2021 Chinese Intelligent Automation Conference

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Attribute reduction refers to selecting some of the most effective attributes from the original attribute sets to reduce the dimensionality of the dataset and optimize the attribute indicators of the system. Rough set theory has made good achievements in attribute reduction as dataset preprocessor, but the current methods are not enough to find the global best subset. The Mayfly algorithm is a new intelligent optimization algorithm, which mainly includes group gathering, crossing, mutation, wedding dance and random walking. In this paper, an attribute reduction algorithm of neighborhood rough set based on improved MA algorithm is proposed. Two Sigmoid mapping functions are used to binary encoding of the mayfly, the switching conditions of the two positions update strategies are set to control migration trend distribution of the mayfly. And then the neighborhood rough set is combined to find the best attribute subset. The UCI and OpenML datasets are used as experiment datasets. The experimental results verify the superiority of the proposed method in finding an optimal reduction. At the same time, the proposed method has higher classification accuracy and faster convergence speed.

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“…To solve the above drawbacks, AFE is a novel way to develop strategies for selecting useful subset features. It can find these useful features in datasets to generate a new set of features for clustering, and can promote the performance by deleting the irrelevant feature and the redundant features [24]. Moreover, it can initialize the number of clusters for clustering and adjust the value of parameters for classification.…”

Section: Introductionmentioning

confidence: 99%

Clustering and Classification Based on Distributed Automatic Feature Engineering for Customer Segmentation

Lee

Chang

et al. 2021

Symmetry

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To beat competition and obtain valuable information, decision-makers must conduct in-depth machine learning or data mining for data analytics. Traditionally, clustering and classification are two common methods used in machine mining. For clustering, data are divided into various groups according to the similarity or common features. On the other hand, classification refers to building a model by given training data, where the target class or label is predicted for the test data. In recent years, many researchers focus on the hybrid of clustering and classification. These techniques have admirable achievements, but there is still room to ameliorate performances, such as distributed process. Therefore, we propose clustering and classification based on distributed automatic feature engineering (AFE) for customer segmentation in this paper. In the proposed algorithm, AFE uses artificial bee colony (ABC) to select valuable features of input data, and then RFM provides the basic data analytics. In AFE, it first initializes the number of cluster k. Moreover, the clustering methods of k-means, Wald method, and fuzzy c-means (FCM) are processed to cluster the examples in variant groups. Finally, the classification method of an improved fuzzy decision tree classifies the target data and generates decision rules for explaining the detail situations. AFE also determines the value of the split number in the improved fuzzy decision tree to increase classification accuracy. The proposed clustering and classification based on automatic feature engineering is distributed, performed in Apache Spark platform. The topic of this paper is about solving the problem of clustering and classification for machine learning. From the results, the corresponding classification accuracy outperforms other approaches. Moreover, we also provide useful strategies and decision rules from data analytics for decision-makers.

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Feature Selection of Grey Wolf Optimizer Based on Quantum Computing and Uncertain Symmetry Rough Set

Cited by 8 publications

References 34 publications

Metaheuristics based COVID-19 detection using medical images: A review

Metaheuristics based COVID-19 detection using medical images: A review

Neighborhood Rough Set Reduction with Improved Mayfly Optimization Algorithm

Clustering and Classification Based on Distributed Automatic Feature Engineering for Customer Segmentation

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