A Supervised Feature Selection Algorithm through Minimum Spanning Tree Clustering

Liu, Qin; Zhang, Jingxiao; Xiao, Jiakai; Zhu, Hongming; Zhao, Qinpei

doi:10.1109/ictai.2014.47

Cited by 11 publications

(5 citation statements)

References 11 publications

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“…Different from other MST based feature selection methods (Liu et al , 2014; Song et al , 2013a), we just use the degree of each node in the MST to obtain the ranking order of each feature, and have not involved any clustering procedure for the feature selection. Compared with the well-known mRMR feature selection method (Peng et al , 2005), our method not only adaptively determines the number of selected features, but also can select the more effective features in discriminating among ROIs from different classes of CISLs.…”

Section: Discussionmentioning

confidence: 99%

“…By computing the MST of the weighted graph, we can obtain the importance of each feature and select the most important features to character the ROIs. Different from the other MST based feature selection methods (Song et al 2013a, Liu et al 2014, our method selects the optimized feature over the global scope, not at the local range. Our method selects features from the whole features, while Song et al (2013a) and Liu et al (2014) cluster the whole features into several groups and then choose features from each group.…”

Section: Introductionmentioning

confidence: 99%

“…Different from the other MST based feature selection methods (Song et al 2013a, Liu et al 2014, our method selects the optimized feature over the global scope, not at the local range. Our method selects features from the whole features, while Song et al (2013a) and Liu et al (2014) cluster the whole features into several groups and then choose features from each group. That likely misses the best global features.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Learning with distribution of optimized features for recognizing common CT imaging signs of lung diseases

Liu

Fei

2016

Phys. Med. Biol.

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Common CT Imaging Signs of Lung Diseases (CISLs) are defined as the imaging signs that frequently appear in lung CT images from patients. CISLs play important roles in the diagnosis of lung diseases. This paper proposes a novel learning method, namely learning with Distribution of Optimized Feature (DOF), to effectively recognize the characteristics of CISLs. We improve the classification performance by learning the optimized features under different distributions. Specifically, we adopt the minimum spanning tree algorithm to capture the relationship between features and discriminant ability of features for selecting the most important features. To overcome the problem of various distributions in one CISL, we propose a hierarchical learning method. First, we use an unsupervised learning method to cluster samples into groups based on their distribution. Second, in each group, we use a supervised learning method to train a model based on their categories of CISLs. Finally, we obtain multiple classification decision from multiple trained models and use majority voting to achieve the final decision. The proposed approach has been implemented on a set of 511 samples captured from human lung CT images and achieves a classification accuracy of 91.96%. The proposed DOF method is effective and can provide a useful tool for computer-aided diagnosis of lung diseases on CT images.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Learning with distribution of optimized features for recognizing common CT imaging signs of lung diseases

Liu

Fei

2016

Phys. Med. Biol.

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“…The first phase uses a minimum spanning tree to cluster all the features, while the second phase selects the most representative features from each cluster to form a subset of features. Liu et al propose a minimum spanning tree-based clustering method, in which measures with information variation are used to assess the correlation and redundancy between features [45]. First, a minimum spanning tree is created based on the correlation between features, and then those long edges are removed to form clusters.…”

Section: Related Workmentioning

confidence: 99%

Differential Privacy High-Dimensional Data Publishing Based on Feature Selection and Clustering

Chu

Zhang³

et al. 2023

Electronics

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As a social information product, the privacy and usability of high-dimensional data are the core issues in the field of privacy protection. Feature selection is a commonly used dimensionality reduction processing technique for high-dimensional data. Some feature selection methods only process some of the features selected by the algorithm and do not take into account the information associated with the selected features, resulting in the usability of the final experimental results not being high. This paper proposes a hybrid method based on feature selection and a cluster analysis to solve the data utility and privacy problems of high-dimensional data in the actual publishing process. The proposed method is divided into three stages: (1) screening features; (2) analyzing the clustering of features; and (3) adaptive noise. This paper uses the Wisconsin Breast Cancer Diagnostic (WDBC) database from UCI’s Machine Learning Library. Using classification accuracy to evaluate the performance of the proposed method, the experiments show that the original data are processed by the algorithm in this paper while protecting the sensitive data information while retaining the contribution of the data to the diagnostic results.

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“…Finally, the feature with the highest correlation with the class label is selected from each cluster. Another study ( Liu et al, 2014 ) under supervised framework similarly used MST for grouping and variation of information for relevance measure. Desired number of features and the pruning rate should be given as inputs in their algorithm.…”

Section: Feature Selection Approachesmentioning

confidence: 99%

Review of feature selection approaches based on grouping of features

Kuzudisli,

Bakir-Gungor,

Bulut

et al. 2023

PeerJ

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With the rapid development in technology, large amounts of high-dimensional data have been generated. This high dimensionality including redundancy and irrelevancy poses a great challenge in data analysis and decision making. Feature selection (FS) is an effective way to reduce dimensionality by eliminating redundant and irrelevant data. Most traditional FS approaches score and rank each feature individually; and then perform FS either by eliminating lower ranked features or by retaining highly-ranked features. In this review, we discuss an emerging approach to FS that is based on initially grouping features, then scoring groups of features rather than scoring individual features. Despite the presence of reviews on clustering and FS algorithms, to the best of our knowledge, this is the first review focusing on FS techniques based on grouping. The typical idea behind FS through grouping is to generate groups of similar features with dissimilarity between groups, then select representative features from each cluster. Approaches under supervised, unsupervised, semi supervised and integrative frameworks are explored. The comparison of experimental results indicates the effectiveness of sequential, optimization-based (i.e., fuzzy or evolutionary), hybrid and multi-method approaches. When it comes to biological data, the involvement of external biological sources can improve analysis results. We hope this work’s findings can guide effective design of new FS approaches using feature grouping.

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A Supervised Feature Selection Algorithm through Minimum Spanning Tree Clustering

Cited by 11 publications

References 11 publications

Learning with distribution of optimized features for recognizing common CT imaging signs of lung diseases

Learning with distribution of optimized features for recognizing common CT imaging signs of lung diseases

Differential Privacy High-Dimensional Data Publishing Based on Feature Selection and Clustering

Review of feature selection approaches based on grouping of features

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