Soft-constrained Laplacian score for semi-supervised multi-label feature selection

Alalga, Abdelouahid; Benabdeslem, Khalid; Taleb, Nora

doi:10.1007/s10115-015-0841-8

Cited by 36 publications

(9 citation statements)

References 39 publications

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“…The key challenge lies in how to use the labeled samples to efficiently process the unlabeled samples. At present, unsupervised feature selection methods mainly focus on clustering-based models, for example, Laplacian score [38], trace ratio [39], and sparsity regularization–based models [40]. For example, a coregularized unsupervised feature selection algorithm was proposed in a study by Zhu et al [41], which was intended to ensure that the selected features could preserve both data distribution and reconstruction.…”

Section: Introductionmentioning

confidence: 99%

A Stroke Risk Detection: Improving Hybrid Feature Selection Method

et al. 2019

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Background Stroke is one of the most common diseases that cause mortality. Detecting the risk of stroke for individuals is critical yet challenging because of a large number of risk factors for stroke. Objective This study aimed to address the limitation of ineffective feature selection in existing research on stroke risk detection. We have proposed a new feature selection method called weighting- and ranking-based hybrid feature selection (WRHFS) to select important risk factors for detecting ischemic stroke. Methods WRHFS integrates the strengths of various filter algorithms by following the principle of a wrapper approach. We employed a variety of filter-based feature selection models as the candidate set, including standard deviation, Pearson correlation coefficient, Fisher score, information gain, Relief algorithm, and chi-square test and used sensitivity, specificity, accuracy, and Youden index as performance metrics to evaluate the proposed method. Results This study chose 792 samples from the electronic records of 13,421 patients in a community hospital. Each sample included 28 features (24 blood test features and 4 demographic features). The results of evaluation showed that the proposed method selected 9 important features out of the original 28 features and significantly outperformed baseline methods. Their cumulative contribution was 0.51. The WRHFS method achieved a sensitivity of 82.7% (329/398), specificity of 80.4% (317/394), classification accuracy of 81.5% (645/792), and Youden index of 0.63 using only the top 9 features. We have also presented a chart for visualizing the risk of having ischemic strokes. Conclusions This study has proposed, developed, and evaluated a new feature selection method for identifying the most important features for building effective and parsimonious models for stroke risk detection. The findings of this research provide several novel research contributions and practical implications.

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Section: Introductionmentioning

confidence: 99%

A Stroke Risk Detection: Improving Hybrid Feature Selection Method

et al. 2019

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“…The multi-label multiclass problem corresponds to multiple labels, and each label has multiple categories of problems. The original single-label learning method cannot be directly used in multi-label learning [53], because every sample of multi-label data is labeled with one or more labels simultaneously. Moreover, the relationship between the labels may be related.…”

Section: Multi-label Learningmentioning

confidence: 99%

A Multi-Objective Multi-Label Feature Selection Algorithm Based on Shapley Value

Dong

Sun

2021

Entropy

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Multi-label learning is dedicated to learning functions so that each sample is labeled with a true label set. With the increase of data knowledge, the feature dimensionality is increasing. However, high-dimensional information may contain noisy data, making the process of multi-label learning difficult. Feature selection is a technical approach that can effectively reduce the data dimension. In the study of feature selection, the multi-objective optimization algorithm has shown an excellent global optimization performance. The Pareto relationship can handle contradictory objectives in the multi-objective problem well. Therefore, a Shapley value-fused feature selection algorithm for multi-label learning (SHAPFS-ML) is proposed. The method takes multi-label criteria as the optimization objectives and the proposed crossover and mutation operators based on Shapley value are conducive to identifying relevant, redundant and irrelevant features. The comparison of experimental results on real-world datasets reveals that SHAPFS-ML is an effective feature selection method for multi-label classification, which can reduce the classification algorithm’s computational complexity and improve the classification accuracy.

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“…The first family is typically implemented over complete label information. Some multi-label feature selection approaches [27], [28] divide the multi-label learning problem into multiple subproblems, which fails to take the label interdependency into account. A majority of approaches try to incorporate label correlations into the process of model construction to help select discriminative features [2], [3], [29], [30].…”

Section: B Multi-label Feature Selectionmentioning

confidence: 99%

A Structure-Induced Framework for Multi-Label Feature Selection With Highly Incomplete Labels

Zhao

2020

IEEE Access

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Feature selection has shown significant promise in improving the effectiveness of multilabel learning by constructing a reduced feature space. Previous studies typically assume that label assignment is complete or partially complete; however, missing-label and unlabeled data are commonplace and accompanying occurrences in real applications due to the high expense of manual annotation and label ambiguity. We call this ''highly incomplete labels'' problem. Such label incompleteness severely damages the inherent label structures and masks true label correlations. In this paper, we propose a novel structureinduced feature selection model to simultaneously identify the most discriminative features and recover the highly incomplete labels. To our best knowledge, it is the first attempt to explore the local density structure of data to capture the intricate feature-label dependency in the highly incomplete learning scenarios. Feature selection is guided by the label structure reconstruction, and highly incomplete labels are recovered via the structure transferred from feature space. In this elegant manner, the processes of selecting discriminative features and recovering incomplete labels are coupled in a unified optimization framework. Comprehensive experiments on public benchmark datasets demonstrate the superiority of the proposed approach.

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Soft-constrained Laplacian score for semi-supervised multi-label feature selection

Cited by 36 publications

References 39 publications

A Stroke Risk Detection: Improving Hybrid Feature Selection Method

A Stroke Risk Detection: Improving Hybrid Feature Selection Method

A Multi-Objective Multi-Label Feature Selection Algorithm Based on Shapley Value

A Structure-Induced Framework for Multi-Label Feature Selection With Highly Incomplete Labels

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