article

Multi-label classification by exploiting label correlations

Authors:

Witold Pedrycz,

Duoqian MiaoAuthors Info & Claims

Expert Systems with Applications: An International Journal, Volume 41, Issue 6

Pages 2989 - 3004

https://doi.org/10.1016/j.eswa.2013.10.030

Published: 01 May 2014 Publication History

Abstract

Nowadays, multi-label classification methods are of increasing interest in the areas such as text categorization, image annotation and protein function classification. Due to the correlation among the labels, traditional single-label classification methods are not directly applicable to the multi-label classification problem. This paper presents two novel multi-label classification algorithms based on the variable precision neighborhood rough sets, called multi-label classification using rough sets (MLRS) and MLRS using local correlation (MLRS-LC). The proposed algorithms consider two important factors that affect the accuracy of prediction, namely the correlation among the labels and the uncertainty that exists within the mapping between the feature space and the label space. MLRS provides a global view at the label correlation while MLRS-LC deals with the label correlation at the local level. Given a new instance, MLRS determines its location and then computes the probabilities of labels according to its location. The MLRS-LC first finds out its topic and then the probabilities of new instance belonging to each class is calculated in related topic. A series of experiments reported for seven multi-label datasets show that MLRS and MLRS-LC achieve promising performance when compared with some well-known multi-label learning algorithms.

References

[1]

Learning multi-label scene classification. Pattern Recognition. v37 i9. 1757-1771.

[2]

Learning multi-label alternating decision trees from texts and data. In: Machine learning and data mining in pattern recognition, Springer Berlin, Heidelberg. pp. 35-49.

[3]

Representing uncertainty on set-valued variables using belief functions. Artificial Intelligence. v174 i7. 479-499.

[4]

Object recognition as chine translation: Learning a lexicon for a fixed image vocabulary. In: Computer vision-ECCV 2002, Springer Berlin, Heidelberg. pp. 97-112.

[5]

Elisseeff, A., & Weston, J. (2001). A kernel method for multi-labelled classification. In Advances in neural information processing systems (pp. 681-687).

[6]

Fan, R. E., Lin, C. J. (2007). A study on threshold selection for multi-label classification. Department of Computer Science, National Taiwan University.

[7]

Discriminative methods for multi-labeled classification. In: Advances in knowledge discovery and data mining, Springer Berlin, Heidelberg. pp. 22-30.

[8]

Mixed feature selection based on granulation and approximation. Knowledge-Based Systems. v21 i4. 294-304.

[9]

Neighborhood classifiers. Expert Systems with Applications. v34 i2. 866-876.

[10]

Label ranking by learning pairwise preferences. Artificial Intelligence. v172 i16. 1897-1916.

[11]

FSKNN: Multi-label text categorization based on fuzzy similarity and k nearest neighbors. Expert Systems with Applications. v39 i3. 2813-2821.

[12]

Rough sets. Communications of the ACM. v38 i11. 88-95.

[13]

Petrovskiy, M. (2006). Paired comparisons method for solving multi-label learning problem. In Hybrid intelligent systems, 2006. HIS'06. sixth international conference on. IEEE (pp. 42-42).

[14]

Classifier chains for multi-label classification. In: Machine learning and knowledge discovery in databases, Springer Berlin, Heidelberg. pp. 254-269.

[15]

Classifier chains for multi-label classification. Machine Learning. v85 i3. 333-359.

[16]

BoosTexter: A boosting-based system for text categorization. Machine learning. v39 i2-3. 135-168.

[17]

he challenge problem for automated detection of 101 semantic concepts in multimedia. In: Proceedings of the 14th annual ACM international conference on multimedia, ACM. pp. 421-430.

[18]

An empirical study of lazy multilabel classification algorithms. In: Artificial Intelligence. Theories, Models and Applications, Springer Berlin, Heidelberg. pp. 401-406.

[19]

Classification of multi-labeled data: A generative approach. In: Machine learning and knowledge discovery in databases, Springer Berlin, Heidelberg. pp. 390-405.

[20]

Multi-label classification of music into emotions. In: ISMIR 2008: Proceedings of the 9th international conference of music information retrieval, Lulu. com. pp. 325

[21]

Mining multi-label data. In: Data mining and knowledge discovery handbook, Springer, US. pp. 667-685.

[22]

Random k-labelsets for multilabel classification. Knowledge and Data Engineering, IEEE Transactions on. v23 i7. 1079-1089.

[23]

Random k-labelsets: An ensemble method for multilabel classification. In: Machine learning: ECML 2007, Springer Berlin, Heidelberg. pp. 406-417.

[24]

MULAN: A java library for multi-label learning. Journal of Machine Learning Research. v12 i7. 2411-2414.

[25]

Semantic annotation and retrieval of music and sound effects. Audio, Speech, and Language Processing, IEEE Transactions on. v16 i2. 467-476.

[26]

Decision trees for hierarchical multi-label classification. Machine Learning. v73 i2. 185-214.

[27]

Data mining: Practical machine learning tools and techniques. Elsevier.

[28]

Relational interpretations of neighborhood operators and rough set approximation operators. Information Sciences. v111 i1. 239-259.

[29]

Neighborhood rough sets based multi-label classification for automatic image annotation. International Journal of Approximate Reasoning.

[30]

Multilabel neural networks with applications to functional genomics and text categorization. Knowledge and Data Engineering, IEEE Transactions on. v18 i10. 1338-1351.

[31]

ML-KNN: A lazy learning approach to multi-label learning. Pattern Recognition. v40 i7. 2038-2048.

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Howlader PDas SLe HSamaras D(2024)Beyond Pixels: Semi-supervised Semantic Segmentation with a Multi-scale Patch-Based Multi-label ClassifierComputer Vision – ECCV 202410.1007/978-3-031-73226-3_20(342-360)Online publication date: 29-Sep-2024
https://dl.acm.org/doi/10.1007/978-3-031-73226-3_20
Zhang ZZhang ZYao JLiu LLi JWu GWu X(2023)Multi-Label Feature Selection Via Adaptive Label Correlation EstimationACM Transactions on Knowledge Discovery from Data10.1145/360456017:9(1-28)Online publication date: 10-Aug-2023
https://dl.acm.org/doi/10.1145/3604560
Zhang ZLiu LLi JWu X(2023)Integrating Global and Local Feature Selection for Multi-Label LearningACM Transactions on Knowledge Discovery from Data10.1145/353219017:1(1-37)Online publication date: 20-Feb-2023
https://dl.acm.org/doi/10.1145/3532190
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Multi-label classification by exploiting label correlations
1. Computing methodologies
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    1. Learning paradigms
      1. Supervised learning

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cover image Expert Systems with Applications: An International Journal

Expert Systems with Applications: An International Journal Volume 41, Issue 6

May, 2014

525 pages

ISSN:0957-4174

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Copyright © Elsevier Ltd © 2013.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 01 May 2014

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Cited By

Howlader PDas SLe HSamaras D(2024)Beyond Pixels: Semi-supervised Semantic Segmentation with a Multi-scale Patch-Based Multi-label ClassifierComputer Vision – ECCV 202410.1007/978-3-031-73226-3_20(342-360)Online publication date: 29-Sep-2024
https://dl.acm.org/doi/10.1007/978-3-031-73226-3_20
Zhang ZZhang ZYao JLiu LLi JWu GWu X(2023)Multi-Label Feature Selection Via Adaptive Label Correlation EstimationACM Transactions on Knowledge Discovery from Data10.1145/360456017:9(1-28)Online publication date: 10-Aug-2023
https://dl.acm.org/doi/10.1145/3604560
Zhang ZLiu LLi JWu X(2023)Integrating Global and Local Feature Selection for Multi-Label LearningACM Transactions on Knowledge Discovery from Data10.1145/353219017:1(1-37)Online publication date: 20-Feb-2023
https://dl.acm.org/doi/10.1145/3532190
Iliadis DDe Baets BWaegeman W(2022)Multi-target prediction for dummies using two-branch neural networksMachine Language10.1007/s10994-021-06104-5111:2(651-684)Online publication date: 1-Feb-2022
https://dl.acm.org/doi/10.1007/s10994-021-06104-5
Bao JWang YCheng Y(2022)Asymmetry label correlation for multi-label learningApplied Intelligence10.1007/s10489-021-02725-452:6(6093-6105)Online publication date: 1-Apr-2022
https://dl.acm.org/doi/10.1007/s10489-021-02725-4
da Silva PGonçalves EPlastino AFreitas A(2022)Distinct Chains for Different Instances: An Effective Strategy for Multi-label Classifier ChainsMachine Learning and Knowledge Discovery in Databases10.1007/978-3-662-44851-9_29(453-468)Online publication date: 10-Mar-2022
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Ke BZhu YLi MShu XQiao RRen B(2022)Hyperspherical Learning in Multi-Label ClassificationComputer Vision – ECCV 202210.1007/978-3-031-19806-9_3(38-55)Online publication date: 23-Oct-2022
https://dl.acm.org/doi/10.1007/978-3-031-19806-9_3
Cheng YSong FQian K(2021)Missing multi-label learning with non-equilibrium based on two-level autoencoderApplied Intelligence10.1007/s10489-020-02140-151:10(6997-7015)Online publication date: 1-Oct-2021
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Ghodratnama SAbrishami Moghaddam H(2021)Content-based image retrieval using feature weighting and C-means clustering in a multi-label classification frameworkPattern Analysis & Applications10.1007/s10044-020-00887-424:1(1-10)Online publication date: 1-Feb-2021
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Guo BTao HHou CYi D(2020)Semi-supervised multi-label feature learning via label enlarged discriminant analysisKnowledge and Information Systems10.1007/s10115-019-01409-362:6(2383-2417)Online publication date: 1-Jun-2020
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