Ensemble constrained Laplacian score for efficient and robust semi-supervised feature selection

Benabdeslem, Khalid; Elghazel, Haytham; Hindawi, Mohammed

doi:10.1007/s10115-015-0901-0

Cited by 14 publications

(8 citation statements)

References 27 publications

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“…In this section, 11 representative traditional feature selection methods are employed to compare with OSFS-KW, including Fisher [36], spectral feature selection [37], Pearson correlation coefficient (PCC) [38], ReliefF [39], Laplacian Score [7], a unsupervised feature selection method with ordinal locality (UFSOL) [40], mutual information (MI) [41], the infinite latent feature selection method (ILFS) [42], lasso regression (Lasso) [43], a fast correlation-based filter method (FCBF) [44], and a correlation based feature selection approach (CFS) [45].…”

Section: Osfs-kw Versus Traditional Feature Selection Methodsmentioning

confidence: 99%

“…Traditional methods are developed based on the assumption that all features are available. Many typical approaches exist, such as ReliefF [5], Fisher Score [6], mutual information (MI) [4], Laplacian Score [7], LASSO [8], and so on [9]. The main benefits of feature selection include speeding up the model training, avoiding overfitting, and reducing the impact of dimensionality during the process of data analysis [4].…”

Section: Introductionmentioning

confidence: 99%

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New Online Streaming Feature Selection Based on Neighborhood Rough Set for Medical Data

Lei

Liang

et al. 2020

Symmetry

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Not all features in many real-world applications, such as medical diagnosis and fraud detection, are available from the start. They are formed and individually flow over time. Online streaming feature selection (OSFS) has recently attracted much attention due to its ability to select the best feature subset with growing features. Rough set theory is widely used as an effective tool for feature selection, specifically the neighborhood rough set. However, the two main neighborhood relations, namely k-neighborhood and neighborhood, cannot efficiently deal with the uneven distribution of data. The traditional method of dependency calculation does not take into account the structure of neighborhood covering. In this study, a novel neighborhood relation combined with k-neighborhood and neighborhood relations is initially defined. Then, we propose a weighted dependency degree computation method considering the structure of the neighborhood relation. In addition, we propose a new OSFS approach named OSFS-KW considering the challenge of learning class imbalanced data. OSFS-KW has no adjustable parameters and pretraining requirements. The experimental results on 19 datasets demonstrate that OSFS-KW not only outperforms traditional methods but, also, exceeds the state-of-the-art OSFS approaches.

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Section: Osfs-kw Versus Traditional Feature Selection Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

New Online Streaming Feature Selection Based on Neighborhood Rough Set for Medical Data

Lei

Liang

et al. 2020

Symmetry

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“…. , 7) and by two semi-supervised feature selection methods namely the semisupervised pairwise constraint-guided sparse (SCGS) learning method [22] and the ensemble constrained Laplacian score (EnsCLS) method [16].…”

Section: Semi-supervised Feature Selectionmentioning

confidence: 99%

Similarity-based constraint score for feature selection

Salmi

Hammouche

Macaire

2020

Knowledge-Based Systems

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“…Problem transformation approaches usually ignore label correlation, or produce a large number of new labels which reduce the effectiveness of the multi-label feature selection approach. Direct feature selection called adapt feature selection improve traditional single-label feature selection approaches to achieve feature selection in multi-label data directly [32]- [34]. In [35], a multi-label feature selection based on Graph-Margin is proposed to utilize graph to represent multi-label data and measure features based on large margin theory.…”

Section: B Multi-label Feature Selection Approachesmentioning

confidence: 99%

Multi-Label Learning With Label Specific Features Using Correlation Information

et al. 2019

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To deal with the problem where each instance is associated with multiple labels, a lot of multi-label learning algorithms have been developed in recent years. Some approaches have been proposed to select label-specific features to utilize discriminate features for multi-label classification. Although label correlation has been considered in learning label-specific features, the critical correlation among instances was less taken into account. In this paper, we proposed a new approach called multi-label learning with label-specific features using correlation information (LSF-CI) to learn label-specific features for each label with the consideration of both correlation information in label space and correlation information in feature space. In the LSF-CI, the instance correlation in feature space is computed by a probabilistic neighborhood graph model, and label correlation in label space is computed by cosine similarity. For multi-label data, the LSF-CI has the capability to select Label-specific features for each label as well as classify an unseen instance into a set of relevant labels. To validate the effectiveness of LSF-CI, we conducted comprehensive experiments on eight multi-label datasets. The experimental results demonstrate that the LSF-CI is capable of selecting compact label-specific features, and achieving a competitive performance in comparison with the performances of the existing multi-label learning approaches.

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Ensemble constrained Laplacian score for efficient and robust semi-supervised feature selection

Cited by 14 publications

References 27 publications

New Online Streaming Feature Selection Based on Neighborhood Rough Set for Medical Data

New Online Streaming Feature Selection Based on Neighborhood Rough Set for Medical Data

Similarity-based constraint score for feature selection

Multi-Label Learning With Label Specific Features Using Correlation Information

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