An efficient unsupervised feature selection procedure through feature clustering

Yan, Xuyang; Nazmi, Shabnam; Erol, Berat A.; Homaifar, Abdollah; Gebru, Biniam; Tunstel, Edward

doi:10.1016/j.patrec.2019.12.022

Cited by 40 publications

(26 citation statements)

References 11 publications

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“…However, recent studies have shown that hybrid feature selection methods can simultaneously take advantage of the efficiency advantages of filter method and the accuracy advantages of warpper method to achieve superior performance [44]. In addition, some studies have also studied the data imbalance problem common in microarray datasets [45,46].…”

Section: Related Workmentioning

confidence: 99%

ILRC : A Hybrid Biomarker Discovering Algorithm Based on Improved L1 Regularization and Clustering in Microarray Data

Xie

Wang

et al. 2021

Preprint

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Background: Finding significant genes or proteins from gene chip data for disease diagnosis and drug development is an important task, and the challenge comes from the curse of the data dimension. It is of great significance to use machine learning methods to find important features from the data and build an accurate classification model. Results: The proposed Mehtod has proved superior to the published advanced hybrid feature selection method and traditional feature selection method on different public microarray data sets. In addition, the results on the cleft lip and palate data set with known biomarkers provided by the cooperative hospital show that compared with other methods, our method can preferentially select these biomarkers. Method: In this paper, a feature selection algorithm ILRC based on clustering and improved L1 regularization is proposed. In this method, the features are first clustered, and the redundant features in the sub-clusters are deleted. Then all the remaining features are iteratively evaluated using ILR, and the final result is output according to the cumulative weight reordering. Conclusion: The proposed method can effectively remove redundant features. The algorithm’s output has high stability and classification accuracy and can potentially select potential biomarkers.

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Section: Related Workmentioning

confidence: 99%

ILRC : A Hybrid Biomarker Discovering Algorithm Based on Improved L1 Regularization and Clustering in Microarray Data

Xie

Wang

et al. 2021

Preprint

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“…A recently developed density-based clustering approach, namely FPS-clustering [31], is used to explore the cluster structure of data without any prior knowledge or parameter optimization. In [26], the authors extended FPS-clustering to feature clustering analysis for completely continuous or discrete features by proposing two different cluster merge schemes. Hence, we develop a new feature clustering approach by integrating the FPS-clustering with a universal cluster merge strategy, which can be applied to both continuous and discrete features.…”

Section: B Density-based Feature Clustering Analysismentioning

confidence: 99%

“…Several recent filter-based feature selection approaches attempted to reduce feature redundancy through feature clustering analysis [22]- [26]. Features are separated into a set of clusters based on their similarity such that highly redundant features are grouped together.…”

Section: Introductionmentioning

confidence: 99%

“…However, few of them considered continuous and discrete features simultaneously. In [26], although the authors employed a modified clustering procedure to perform continuous or discrete feature selection separately, it assumed that all features are homogeneous. Besides, the relevance of features with respect to the class label is not considered.…”

Section: Introductionmentioning

confidence: 99%

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A Supervised Feature Selection Method For Mixed-Type Data using Density-based Feature Clustering

Yan¹,

Sarkar²,

Gebru³

et al. 2021

Preprint

Self Cite

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Feature selection methods are widely used to address the high computational overheads and curse of dimensionality in classifying high-dimensional data. Most conventional feature selection methods focus on handling homogeneous features, while real-world datasets usually have a mixture of continuous and discrete features. Some recent mixed-type feature selection studies only select features with high relevance to class labels and ignore the redundancy among features. The determination of an appropriate feature subset is also a challenge. In this paper, a supervised feature selection method using density-based feature clustering (SFSDFC) is proposed to obtain an appropriate final feature subset for mixed-type data. SFSDFC decomposes the feature space into a set of disjoint feature clusters using a novel density-based clustering method. Then, an effective feature selection strategy is employed to obtain a subset of important features with minimal redundancy from those feature clusters. Extensive experiments as well as comparison studies with five state-of-the-art methods are conducted on SFSDFC using thirteen real-world benchmark datasets and results justify the efficacy of the SFSDFC method.

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“…However, recent studies have shown that hybrid feature selection methods can simultaneously take the efficiency advantages of filter method and the accuracy advantages of warpper method to achieve superior performance [ 44 ]. In addition, some studies have also figured out the data imbalance problem commonly appeared in microarray datasets [ 45 , 46 ].…”

Section: Introductionmentioning

confidence: 99%

ILRC: a hybrid biomarker discovery algorithm based on improved L1 regularization and clustering in microarray data

Xie

Wang

et al. 2021

BMC Bioinformatics

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Background Finding significant genes or proteins from gene chip data for disease diagnosis and drug development is an important task. However, the challenge comes from the curse of the data dimension. It is of great significance to use machine learning methods to find important features from the data and build an accurate classification model. Results The proposed method has proved superior to the published advanced hybrid feature selection method and traditional feature selection method on different public microarray data sets. In addition, the biomarkers selected using our method show a match to those provided by the cooperative hospital in a set of clinical cleft lip and palate data. Method In this paper, a feature selection algorithm ILRC based on clustering and improved L1 regularization is proposed. The features are firstly clustered, and the redundant features in the sub-clusters are deleted. Then all the remaining features are iteratively evaluated using ILR. The final result is given according to the cumulative weight reordering. Conclusion The proposed method can effectively remove redundant features. The algorithm’s output has high stability and classification accuracy, which can potentially select potential biomarkers.

show abstract

An efficient unsupervised feature selection procedure through feature clustering

Cited by 40 publications

References 11 publications

ILRC : A Hybrid Biomarker Discovering Algorithm Based on Improved L1 Regularization and Clustering in Microarray Data

ILRC : A Hybrid Biomarker Discovering Algorithm Based on Improved L1 Regularization and Clustering in Microarray Data

A Supervised Feature Selection Method For Mixed-Type Data using Density-based Feature Clustering

ILRC: a hybrid biomarker discovery algorithm based on improved L1 regularization and clustering in microarray data

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