Binary Hybrid Differential Evolution Algorithm for Multi-label Feature Selection

Varghese, Nelson Vithayathil; Singh, Amritpal; Suresh, A.; Rahnamayan, Shahryar

doi:10.1109/smc42975.2020.9283208

Cited by 8 publications

(3 citation statements)

References 34 publications

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“…[35] first applied genetic algorithm to FS. Subsequently, various typical evolutionary or swarm intelligence algorithms are successively applied to FS [15–18]. In 2016, Xue et al.…”

Section: Related Workmentioning

confidence: 99%

“…In 1989, Siedlecki et al [35] first applied genetic algorithm to FS. Subsequently, various typical evolutionary or swarm intelligence algorithms are successively applied to FS [15][16][17][18]. In 2016, Xue et al [8] summarised the current research status of evolutionary FS in detail.…”

Section: Evolutionary Feature Selectionmentioning

confidence: 99%

“…Since it can find the optimal or sub-optimal solutions of the problem through global search strategy, it has become a hot technology to solve FS problems in recent years [8]. Various typical evolutionary optimisation algorithms, such as genetic algorithm [15], differential evolution [16], ant colony algorithm [17], and artificial bee colony algorithm [18], have been successfully applied in FS problems. As a relatively new evolutionary optimisation technique, particle swarm optimisation (PSO) [19] has the advantages of simple concept, convenient implementation and fast convergence, so scholars have tried to apply it to FS problems [20,21].…”

mentioning

confidence: 99%

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An embedded vertical‐federated feature selection algorithm based on particle swarm optimisation

Zhang

Gao

et al. 2022

CAAI Trans on Intel Tech

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In real life, a large amount of data describing the same learning task may be stored in different institutions (called participants), and these data cannot be shared among participants due to privacy protection. The case that different attributes/features of the same instance are stored in different institutions is called vertically distributed data. The purpose of vertical‐federated feature selection (FS) is to reduce the feature dimension of vertical distributed data jointly without sharing local original data so that the feature subset obtained has the same or better performance as the original feature set. To solve this problem, in the paper, an embedded vertical‐federated FS algorithm based on particle swarm optimisation (PSO‐EVFFS) is proposed by incorporating evolutionary FS into the SecureBoost framework for the first time. By optimising both hyper‐parameters of the XGBoost model and feature subsets, PSO‐EVFFS can obtain a feature subset, which makes the XGBoost model more accurate. At the same time, since different participants only share insensitive parameters such as model loss function, PSO‐EVFFS can effectively ensure the privacy of participants' data. Moreover, an ensemble ranking strategy of feature importance based on the XGBoost tree model is developed to effectively remove irrelevant features on each participant. Finally, the proposed algorithm is applied to 10 test datasets and compared with three typical vertical‐federated learning frameworks and two variants of the proposed algorithm with different initialisation strategies. Experimental results show that the proposed algorithm can significantly improve the classification performance of selected feature subsets while fully protecting the data privacy of all participants.

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“…[35] first applied genetic algorithm to FS. Subsequently, various typical evolutionary or swarm intelligence algorithms are successively applied to FS [15–18]. In 2016, Xue et al.…”

Section: Related Workmentioning

confidence: 99%