Multilabel feature selection: A comprehensive review and guiding experiments

Kashef, Shima; Nezamabadi‐pour, Hossein; Nikpour, Bahareh

doi:10.1002/widm.1240

Cited by 94 publications

(37 citation statements)

References 133 publications

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“…F-score [ 60 ] is another feature selection approach that quantifies the discriminative ability of variables (features) based on the following equation:

where c is the number of classes, n is the number of features,

number of samples of feature i in class k , and

is the

training sample for feature i in class k . Features are ranked based on F-score , such that a higher F-score value corresponds to most discriminative feature.…”

Section: Architecture Of MI Based Bcimentioning

confidence: 99%

A Comprehensive Review on Critical Issues and Possible Solutions of Motor Imagery Based Electroencephalography Brain-Computer Interface

Singh

Hussain

Lal

et al. 2021

Sensors

103

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Motor imagery (MI) based brain–computer interface (BCI) aims to provide a means of communication through the utilization of neural activity generated due to kinesthetic imagination of limbs. Every year, a significant number of publications that are related to new improvements, challenges, and breakthrough in MI-BCI are made. This paper provides a comprehensive review of the electroencephalogram (EEG) based MI-BCI system. It describes the current state of the art in different stages of the MI-BCI (data acquisition, MI training, preprocessing, feature extraction, channel and feature selection, and classification) pipeline. Although MI-BCI research has been going for many years, this technology is mostly confined to controlled lab environments. We discuss recent developments and critical algorithmic issues in MI-based BCI for commercial deployment.

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“…F-score [ 60 ] is another feature selection approach that quantifies the discriminative ability of variables (features) based on the following equation:

where c is the number of classes, n is the number of features,

number of samples of feature i in class k , and

is the

training sample for feature i in class k . Features are ranked based on F-score , such that a higher F-score value corresponds to most discriminative feature.…”

Section: Architecture Of MI Based Bcimentioning

confidence: 99%

A Comprehensive Review on Critical Issues and Possible Solutions of Motor Imagery Based Electroencephalography Brain-Computer Interface

Singh

Hussain

Lal

et al. 2021

Sensors

103

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“…The one error, coverage, ranking loss, and average precision were used to evaluate the overall performance of all representation methods in ATC classification. These metrics are defined in detail in [131], and are frequently used for evaluating the performance of ATC classifiers. The area under precision recall (AUPR) curve and the area under receiver operating characteristic(AUROC) curve were employed to evaluate the performance of all representation methods in bio-link prediction.…”

Section: Experiments Settings and Evaluation Metricsmentioning

confidence: 99%

Network Representation Learning-Based Drug Mechanism Discovery and Anti-Inflammatory Response Against COVID-19

Wang

Xin²,

Xu³

et al. 2021

Preprint

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<p>Recent studies have been demonstrated that the excessive inflammatory response is an important factor of death in COVID-19 patients. In this study, we proposed a network representation learning-based methodology, termed AIdrug2cov, to discover drug mechanism and anti-inflammatory response for patients with COVID-19. This work explores the multi-hub characteristic of a heterogeneous drug network integrating 8 unique networks. Inspired by the multi-hub characteristic, we design three billion special meta paths to train a deep representation model for learning low-dimensional vectors that integrate long-range structure dependency and complex semantic relation among network nodes. Using the representation vectors, AIdrug2cov identifies 40 potential targets and 22 high-confidence drugs that bind to tumor necrosis factor(TNF)-α or interleukin(IL)-6 to prevent excessive inflammatory responses in COVID-19 patients. Finally, we analyze mechanisms of action based on PubMed publications and ongoing clinical trials, and explore the possible binding modes between the new predicted drugs and targets via docking program. In addition, the results in 5 pharmacological application suggested that AIdrug2cov significantly outperforms 5 other state-of-the-art network representation approaches, future demonstrating the availability of AIdrug2cov in drug development field. In summary, AIdrug2cov is practically useful for accelerating COVID-19 therapeutic development. The source code and data can be downloaded from https://github.com/pengsl-lab/AIdrug2cov.git.</p>

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“…Some comprehensive literature reviews and research articles [ 15 , 16 , 17 , 18 ] have discussed the problem of feature selection (FS) in the MLC problems. There are different methods used to select relevant features from the MLC datasets.…”

Section: Preliminariesmentioning

confidence: 99%

Partial Classifier Chains with Feature Selection by Exploiting Label Correlation in Multi-Label Classification

Wang

Wan

et al. 2020

Entropy

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Multi-label classification (MLC) is a supervised learning problem where an object is naturally associated with multiple concepts because it can be described from various dimensions. How to exploit the resulting label correlations is the key issue in MLC problems. The classifier chain (CC) is a well-known MLC approach that can learn complex coupling relationships between labels. CC suffers from two obvious drawbacks: (1) label ordering is decided at random although it usually has a strong effect on predictive performance; (2) all the labels are inserted into the chain, although some of them may carry irrelevant information that discriminates against the others. In this work, we propose a partial classifier chain method with feature selection (PCC-FS) that exploits the label correlation between label and feature spaces and thus solves the two previously mentioned problems simultaneously. In the PCC-FS algorithm, feature selection is performed by learning the covariance between feature set and label set, thus eliminating the irrelevant features that can diminish classification performance. Couplings in the label set are extracted, and the coupled labels of each label are inserted simultaneously into the chain structure to execute the training and prediction activities. The experimental results from five metrics demonstrate that, in comparison to eight state-of-the-art MLC algorithms, the proposed method is a significant improvement on existing multi-label classification.

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Multilabel feature selection: A comprehensive review and guiding experiments

Cited by 94 publications

References 133 publications

A Comprehensive Review on Critical Issues and Possible Solutions of Motor Imagery Based Electroencephalography Brain-Computer Interface

A Comprehensive Review on Critical Issues and Possible Solutions of Motor Imagery Based Electroencephalography Brain-Computer Interface

Network Representation Learning-Based Drug Mechanism Discovery and Anti-Inflammatory Response Against COVID-19

Partial Classifier Chains with Feature Selection by Exploiting Label Correlation in Multi-Label Classification

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