Adaptive Data Structure Regularized Multiclass Discriminative Feature Selection

Fan, Mingyu; Zhang, Xiaoqin; Hu, Jie; Gu, Nannan; Tao, Dacheng

doi:10.1109/tnnls.2021.3071603

Cited by 36 publications

(5 citation statements)

References 57 publications

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“…Owing to its strong optimization capability, the proposed GOMGBO can also be applied to other optimization problems, such as regression tasks, 141 medical diagnosis, [142][143][144][145] covert communication systems, [146][147][148] service ecosystem, 149,150 image editing, [151][152][153] energy storage planning and scheduling, 154 social recommendation and quality-of-service (QoS)-aware service composition, [155][156][157] active surveillance, 158 pedestrian dead reckoning, 159 evaluation of human lower limb motions, 160 image super resolution, [161][162][163] sentiment classification, 164 data-to-text generation, 165 crowd sensing, 166 and feature selection. [167][168][169]…”

Section: Experimental Results Of the Mammographic Data Setmentioning

confidence: 99%

Gaussian bare‐bones gradient‐based optimization: Towards mitigating the performance concerns

Qiao

Shan

Jiang

et al. 2021

Int J of Intelligent Sys

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Gradient‐based optimizer (GBO) is a metaphor‐free mathematic‐based algorithm proposed in recent years. Encouraged by the gradient‐based Newton's method, this algorithm combines with population‐based evolutionary methods. The disadvantage of the traditional GBO algorithm is that the global search ability of the algorithm is too strong, and the local search ability is too weak; accordingly, it is difficult to obtain the global optimal solution efficiently. Therefore, a new improved GBO algorithm (GOMGBO) is developed to mitigate such performance concerns by introducing a Gaussian bare‐bones mechanism, an opposition‐based learning mechanism, and a moth spiral mechanism enhanced GBO algorithm. The proposed GOMGBO has been compared against many famous methods and improved variants on 30 benchmark functions. The experimental results show that GOMGBO has apparent advantages in convergence speed and precision. In addition, this paper analyzes the balance and diversity of the GOMGBO algorithm and compares GOMGBO with other algorithms on several engineering problems. The experimental results show that the GOMGBO algorithm is also better than the competitive algorithm in engineering problems. This study uses the GOMGBO algorithm to optimize kernel extreme learning machine (KELM), and a new GOMGBO‐KELM model is proposed. The model is used to deal with four clinical disease diagnosis problems. Compared with GBO‐KELM, back propagation neural network algorithm, and other models, comparative experiments show that GOMGBO‐KELM has high performance in dealing with practical cases. We invite the community to investigate further our method for solving problems more efficiently with reasonable speed and efficiency. Readers of this study can refer to https://aliasgharheidari.com for any guidance about the proposed GOMGBO method.

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Section: Experimental Results Of the Mammographic Data Setmentioning

confidence: 99%

Gaussian bare‐bones gradient‐based optimization: Towards mitigating the performance concerns

Qiao

Shan

Jiang

et al. 2021

Int J of Intelligent Sys

View full text Add to dashboard Cite

show abstract

“…After testing with different categories of data sets and more complex and new practical problems, careful implementation and correct setting of the initial parameters and operators of the method used will help obtain better results. In the near future, the proposed SCGWO can also be applied to other problems, such as covert communication system, [131][132][133] service ecosystem, 134,135 image editing, [136][137][138] image dehazing, [139][140][141] large scale network analysis, 142 energy storage planning and scheduling, 143 social recommendation and QOS-aware service composition, [144][145][146] active surveillance, 147 sentiment classification, 148 data-to-text generation, 149 crowd sensing, 150 feature selection, [151][152][153] location-based services, 154,155 kayak cycle phase segmentation, 156 human motion capture, 157 and information retrieval services. [158][159][160]…”

Section: Discussionmentioning

confidence: 99%

Chaotic diffusion‐limited aggregation enhanced grey wolf optimizer: Insights, analysis, binarization, and feature selection

Heidari

Zhang

et al. 2021

Int J of Intelligent Sys

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Grey wolf optimization (GWO) is a widely used metaheuristic method. It has limited searching potential when solving the majority of function optimization problems. This paper proposes a new variant of GWO, named SCGWO, which combines GWO with an improved spread strategy and a chaotic local search (CLS) mechanism to overcome these performance limitations. In detail, a spread strategy is introduced into the basic GWO to change the search agent's ability to avoid the local optima,

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“…Benchmark datasets serve as widely acknowledged instruments for assessing the performance of various technologies against uniform criteria 58 , 59 . These datasets facilitate the evaluation of different technological dimensions, determining which technology excels over others across multiple domains 60 , 61 . Secondly, the algorithms are tested on different number of evaluations as well as different population sizes while controlling for other variables, and the complexity of SMA_FBI is also investigated and explained.…”

Section: Methodsmentioning

confidence: 99%

Advancing forensic-based investigation incorporating slime mould search for gene selection of high-dimensional genetic data

Qiu,

Heidari,

Chen

et al. 2024

Sci Rep

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Modern medicine has produced large genetic datasets of high dimensions through advanced gene sequencing technology, and processing these data is of great significance for clinical decision-making. Gene selection (GS) is an important data preprocessing technique that aims to select a subset of feature information to improve performance and reduce data dimensionality. This study proposes an improved wrapper GS method based on forensic-based investigation (FBI). The method introduces the search mechanism of the slime mould algorithm in the FBI to improve the original FBI; the newly proposed algorithm is named SMA_FBI; then GS is performed by converting the continuous optimizer to a binary version of the optimizer through a transfer function. In order to verify the superiority of SMA_FBI, experiments are first executed on the 30-function test set of CEC2017 and compared with 10 original algorithms and 10 state-of-the-art algorithms. The experimental results show that SMA_FBI is better than other algorithms in terms of finding the optimal solution, convergence speed, and robustness. In addition, BSMA_FBI (binary version of SMA_FBI) is compared with 8 binary algorithms on 18 high-dimensional genetic data from the UCI repository. The results indicate that BSMA_FBI is able to obtain high classification accuracy with fewer features selected in GS applications. Therefore, SMA_FBI is considered an optimization tool with great potential for dealing with global optimization problems, and its binary version, BSMA_FBI, can be used for GS tasks.

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Adaptive Data Structure Regularized Multiclass Discriminative Feature Selection

Cited by 36 publications

References 57 publications

Gaussian bare‐bones gradient‐based optimization: Towards mitigating the performance concerns

Gaussian bare‐bones gradient‐based optimization: Towards mitigating the performance concerns

Chaotic diffusion‐limited aggregation enhanced grey wolf optimizer: Insights, analysis, binarization, and feature selection

Advancing forensic-based investigation incorporating slime mould search for gene selection of high-dimensional genetic data

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