Joint multi-label classification and label correlations with missing labels and feature selection

He, Zhifen; Yang, Ming; Gao, Yang; Liu, Huidong; Yin, Yilong

doi:10.1016/j.knosys.2018.08.018

Cited by 75 publications

(34 citation statements)

References 21 publications

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“…To effectively evaluate the classification results of all compared feature selection algorithms, six evaluation metrics are employed, which include the number of selected features (N ), the Average Precision (AP), the Coverage (CV), the Hamming Loss (HL), the One Error (OE), and the Ranking Loss (RL) [43], [44]. The larger the value of AP is, the better the classification performance is, and the optimal value is 1.…”

Section: Experimental Results and Analysis A Experiments Preparationmentioning

confidence: 99%

Multilabel Feature Selection Using Relief and Minimum Redundancy Maximum Relevance Based on Neighborhood Rough Sets

Miao-miao

Sun

et al. 2020

IEEE Access

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Recently, multilabel classification is of increasing interest in machine learning and artificial intelligence. However, the distances of samples in most Relief methods easily result in heterogeneous or similar samples abnormal when the distances are very large. Besides, the classification margin as a neighborhood radius for some reduction algorithms may be meaningless when the margin is too large. To overcome these drawbacks, this paper presents a multilabel feature selection method using the improved Relief and minimum redundancy maximum relevance (MRMR) based on neighborhood rough sets. First, the number of heterogeneous and similar samples is introduced to improve the label weighting method which can eliminate the influence of the large distances of samples. By combining with the new label weighting, the distances between the sample and its nearest-neighbor heterogeneous sample and between the sample and its nearest-neighbor similar sample are presented to develop a new feature weighting method. Second, the number of heterogeneous and similar samples continues to be used to improve the classification margin, thereby constraining the neighborhood radius, based on which the neighborhood approximation accuracy is constructed to effectively measure the uncertainty of samples in the boundary region and the completeness of knowledge. Third, by integrating with the new neighborhood approximation accuracy, two types of mutual information between features and labels and among features are proposed, and then the mutual informationbased MRMR model is investigated to evaluate the significance of features. Finally, a multilabel feature selection algorithm is designed for improving the classification performance of multilabel data. Experimental results on thirteen public datasets illustrate the effectiveness of our developed algorithm that can select the significant features and achieve great performance for multilabel datasets.

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Section: Experimental Results and Analysis A Experiments Preparationmentioning

confidence: 99%

Multilabel Feature Selection Using Relief and Minimum Redundancy Maximum Relevance Based on Neighborhood Rough Sets

Miao-miao

Sun

et al. 2020

IEEE Access

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“…A semisupervised multilabel method is proposed in [ 23 ], while label correlations are incorporated by modifying the loss function. Multilabel classification and label correlations learning can also be realized in a joint learning framework [ 24 , 25 ].…”

Section: Related Workmentioning

confidence: 99%

Hierarchical Network with Label Embedding for Contextual Emotion Recognition

Deng

Ren

2021

Research

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Emotion recognition has been used widely in various applications such as mental health monitoring and emotional management. Usually, emotion recognition is regarded as a text classification task. Emotion recognition is a more complex problem, and the relations of emotions expressed in a text are nonnegligible. In this paper, a hierarchical model with label embedding is proposed for contextual emotion recognition. Especially, a hierarchical model is utilized to learn the emotional representation of a given sentence based on its contextual information. To give emotion correlation-based recognition, a label embedding matrix is trained by joint learning, which contributes to the final prediction. Comparison experiments are conducted on Chinese emotional corpus RenCECps, and the experimental results indicate that our approach has a satisfying performance in textual emotion recognition task.

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“…Label-specific feature learning [9], [16], [20], [29]- [31] aims to improve the performance of multi-label classification by learning a discriminative data representation for each label respectively. Multi-label learning with missing labels solve the problem that some entities are missing in the label matrix [8], [32]- [38]. By surveying previous success on multi-label learning, it is noted that existing approaches mainly focused on a fixed set of observed labels.…”

Section: Related Workmentioning

confidence: 99%

Multi-Label Learning With Hidden Labels

Huang

Rui

et al. 2020

IEEE Access

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In multi-label learning, each object is represented by a single instance and associated with multiple labels simultaneously. Existing multi-label learning approaches mainly construct classification models with a fixed set of target labels (observed labels). However, in the big data era, it is difficult to provide a fully complete label set for a data set. In some real applications, there are multiple labels hidden in the data set, especially for those large-scale data sets. In this paper, a novel approach named MLLHL is proposed to not only discover the hidden labels in the training data but also predict these hidden labels and observed labels for unseen examples simultaneously. We assume that the observed labels are just a subset of labels which are selected from the full label set, and the rest ones are omitted by the annotators during the labeling stage. Extensive experiments show the competitive performance of MLLHL against other stateof-the-art multi-label learning approaches. INDEX TERMS Multi-label learning, discovering hidden labels.

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Joint multi-label classification and label correlations with missing labels and feature selection

Cited by 75 publications

References 21 publications

Multilabel Feature Selection Using Relief and Minimum Redundancy Maximum Relevance Based on Neighborhood Rough Sets

Multilabel Feature Selection Using Relief and Minimum Redundancy Maximum Relevance Based on Neighborhood Rough Sets

Hierarchical Network with Label Embedding for Contextual Emotion Recognition

Multi-Label Learning With Hidden Labels

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