Semi-supervised local feature selection for data classification

Li, Zechao; Tang, Jinhui

doi:10.1007/s11432-020-3063-0

Cited by 39 publications

(5 citation statements)

References 30 publications

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“…We first used five image classification datasets to test the classification performance of the proposed method and then employed two image datasets and two subsets of UCI data to verify the clustering performance of the proposed method. In the experiment, we compared our proposed method with some contemporary UFS and SSFS methods, including two UFS methods (SPNFSR [56] and NNSAFS [57]) and six SSFS methods (RLSR [19], FDEFS [50], GS 3 FS [43], S2LFS [44], AGLRM [47], and ASLCGLFS [48]).…”

Section: Experiments and Analysismentioning

confidence: 99%

“…The second problem is that the spatial distribution of the sample label information is not sufficiently considered, resulting in the weak discriminative ability of the selected features, which further leads to poor classification or clustering performance. To alleviate this issue, label propagation (LP) has been incorporated into the FS methods [42][43][44]. However, since LP is also a graph-learning-based algorithm, the quality of the learned graph affects the performance to some extent.…”

Section: Introductionmentioning

confidence: 99%

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SFS-AGGL: Semi-Supervised Feature Selection Integrating Adaptive Graph with Global and Local Information

Yi,

Zhang,

Zhang

et al. 2024

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As the feature dimension of data continues to expand, the task of selecting an optimal subset of features from a pool of limited labeled data and extensive unlabeled data becomes more and more challenging. In recent years, some semi-supervised feature selection methods (SSFS) have been proposed to select a subset of features, but they still have some drawbacks limiting their performance, for e.g., many SSFS methods underutilize the structural distribution information available within labeled and unlabeled data. To address this issue, we proposed a semi-supervised feature selection method based on an adaptive graph with global and local constraints (SFS-AGGL) in this paper. Specifically, we first designed an adaptive graph learning mechanism that can consider both the global and local information of samples to effectively learn and retain the geometric structural information of the original dataset. Secondly, we constructed a label propagation technique integrated with the adaptive graph learning in SFS-AGGL to fully utilize the structural distribution information of both labeled and unlabeled data. The proposed SFS-AGGL method is validated through classification and clustering tasks across various datasets. The experimental results demonstrate its superiority over existing benchmark methods, particularly in terms of clustering performance.

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Section: Experiments and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

SFS-AGGL: Semi-Supervised Feature Selection Integrating Adaptive Graph with Global and Local Information

Yi,

Zhang,

Zhang

et al. 2024

Information

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“…The majority of existing approaches are designed for only one type of community structure. For instance, the methods based on random walk for assortative community, [18] the spectral methods for assortative community [16] and for bipartite community, [14] the optimization methods for assortative community, [17] bipartite community, [9] and both of them, [26] the methods based on statistical inference for assortative community [21,22,24,25] and bipartite community, [23] and the deep learning methods for assortative community. [28,29] To adapt one approach for another community structure considerable work is needed.…”

Section: Related Workmentioning

confidence: 99%

“…But the community exploration through statistical models differs from data classification by feature selection. [26] Our model aims to learn the latent and unknown features described by our assumption, whereas feature selection for data classification aims to learn a subset of features that have already been given.…”

Section: Introductionmentioning

confidence: 99%

Pitman–Yor process mixture model for community structure exploration considering latent interaction patterns*

Wang¹,

Li²

2021

Chinese Phys. B

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The statistical model for community detection is a promising research area in network analysis. Most existing statistical models of community detection are designed for networks with a known type of community structure, but in many practical situations, the types of community structures are unknown. To cope with unknown community structures, diverse types should be considered in one model. We propose a model that incorporates the latent interaction pattern, which is regarded as the basis of constructions of diverse community structures by us. The interaction pattern can parameterize various types of community structures in one model. A collapsed Gibbs sampling inference is proposed to estimate the community assignments and other hyper-parameters. With the Pitman–Yor process as a prior, our model can automatically detect the numbers and sizes of communities without a known type of community structure beforehand. Via Bayesian inference, our model can detect some hidden interaction patterns that offer extra information for network analysis. Experiments on networks with diverse community structures demonstrate that our model outperforms four state-of-the-art models.

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“…It offers quick training and effective learning outcomes, which are important characteristics. Due to this, the distributed extreme learning machine (D-ELM) avoids reading all samples into memory at once by partitioning matrix operations, but it also resolves the issue of memory shortage while training vast amounts of sample data [ 6 , 7 ]. Every time they run, they only train an ELM network with a certain amount of hidden layer nodes.…”

Section: Introductionmentioning

confidence: 99%

Towards an Optimal KELM Using the PSO-BOA Optimization Strategy with Applications in Data Classification

et al. 2023

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The features of the kernel extreme learning machine—efficient processing, improved performance, and less human parameter setting—have allowed it to be effectively used to batch multi-label classification tasks. These classic classification algorithms must at present contend with accuracy and space–time issues as a result of the vast and quick, multi-label, and concept drift features of the developing data streams in the practical application sector. The KELM training procedure still has a difficulty in that it has to be repeated numerous times independently in order to maximize the model’s generalization performance or the number of nodes in the hidden layer. In this paper, a kernel extreme learning machine multi-label data classification method based on the butterfly algorithm optimized by particle swarm optimization is proposed. The proposed algorithm, which fully accounts for the optimization of the model generalization ability and the number of hidden layer nodes, can train multiple KELM hidden layer networks at once while maintaining the algorithm’s current time complexity and avoiding a significant number of repeated calculations. The simulation results demonstrate that, in comparison to the PSO-KELM, BBA-KELM, and BOA-KELM algorithms, the PSOBOA-KELM algorithm proposed in this paper can more effectively search the kernel extreme learning machine parameters and more effectively balance the global and local performance, resulting in a KELM prediction model with a higher prediction accuracy.

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Semi-supervised local feature selection for data classification

Cited by 39 publications

References 30 publications

SFS-AGGL: Semi-Supervised Feature Selection Integrating Adaptive Graph with Global and Local Information

SFS-AGGL: Semi-Supervised Feature Selection Integrating Adaptive Graph with Global and Local Information

Pitman–Yor process mixture model for community structure exploration considering latent interaction patterns*

Towards an Optimal KELM Using the PSO-BOA Optimization Strategy with Applications in Data Classification

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