Non-Aligned Multi-View Multi-Label Classification via Learning View-Specific Labels

Zhao, Dawei; Gao, Qingwei; Lu, Yu-Ting; Sun, Da

doi:10.1109/tmm.2022.3219650

Cited by 18 publications

(2 citation statements)

References 55 publications

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“…When the same comment belongs to many classes (multilabel), this becomes a more challenging task, especially when datasets are imbalanced [2,3]. Multilabel Arabic Text Classification (ML-ATC) is a crucial issue for the Arabic language because it is widely employed in many different fields, including sentiment analysis, bioinformatics, image classification, and scene classification [4].…”

Section: Introductionmentioning

confidence: 99%

An Optimized Arabic Multilabel Text Classification Approach Using Genetic Algorithm and Ensemble Learning

Alzanin,

Gumaei,

Haque

et al. 2023

Applied Sciences

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Multilabel classification of Arabic text is an important task for understanding and analyzing social media content. It can enable the categorization and monitoring of social media posts, the detection of important events, the identification of trending topics, and the gaining of insights into public opinion and sentiment. However, multilabel classification of Arabic contents can present a certain challenge due to the high dimensionality of the representation and the unique characteristics of the Arabic language. In this paper, an effective approach is proposed for Arabic multilabel classification using a metaheuristic Genetic Algorithm (GA) and ensemble learning. The approach explores the effect of Arabic text representation on classification performance using both Bag of Words (BOW) and Term Frequency-Inverse Document Frequency (TF-IDF) methods. Moreover, it compares the performance of ensemble learning methods such as the Extra Trees Classifier (ETC) and Random Forest Classifier (RFC) against a Logistic Regression Classifier (LRC) as a single and ensemble classifier. We evaluate the approach on a new public dataset, namely, the MAWQIF dataset. The MAWQIF is the first multilabel Arabic dataset for target-specific stance detection. The experimental results demonstrate that the proposed approach outperforms the related work on the same dataset, achieving 80.88% for sentiment classification and 68.76% for multilabel tasks in terms of the F1-score metric. In addition, the data augmentation with feature selection improves the F1-score result of the ETC from 65.62% to 68.80%. The study shows the ability of the GA-based feature selection with ensemble learning to improve the classification of multilabel Arabic text.

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Section: Introductionmentioning

confidence: 99%

An Optimized Arabic Multilabel Text Classification Approach Using Genetic Algorithm and Ensemble Learning

Alzanin,

Gumaei,

Haque

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…In the related works section, we have introduced iMVWL, NAIM3L, DICNet, GLOCAL, and CDMM. In addition to these five methods, we take three additional methods, namely C2AE (Yeh et al 2017), DM2L (Ma and Chen 2021), and LVSL (Zhao et al 2022a). C2AE is a deep neural network model that integrates canonical correlation analysis and autoencoder architectures for effective and robust multi-label classification.…”

mentioning

confidence: 99%

Attention-Induced Embedding Imputation for Incomplete Multi-View Partial Multi-Label Classification

Liu,

Jia,

Wen

et al. 2024

AAAI

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As a combination of emerging multi-view learning methods and traditional multi-label classification tasks, multi-view multi-label classification has shown broad application prospects. The diverse semantic information contained in heterogeneous data effectively enables the further development of multi-label classification. However, the widespread incompleteness problem on multi-view features and labels greatly hinders the practical application of multi-view multi-label classification. Therefore, in this paper, we propose an attention-induced missing instances imputation technique to enhance the generalization ability of the model. Different from existing incomplete multi-view completion methods, we attempt to approximate the latent features of missing instances in embedding space according to cross-view joint attention, instead of recovering missing views in kernel space or original feature space. Accordingly, multi-view completed features are dynamically weighted by the confidence derived from joint attention in the late fusion phase. In addition, we propose a multi-view multi-label classification framework based on label-semantic feature learning, utilizing the statistical weak label correlation matrix and graph attention network to guide the learning process of label-specific features. Finally, our model is compatible with missing multi-view and partial multi-label data simultaneously and extensive experiments on five datasets confirm the advancement and effectiveness of our embedding imputation method and multi-view multi-label classification model.

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Multi-label learning of missing labels using label-specific features: an embedded packaging method

Zhao,

Tan,

Sun

et al. 2023

Appl Intell

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Non-Aligned Multi-View Multi-Label Classification via Learning View-Specific Labels

Cited by 18 publications

References 55 publications

An Optimized Arabic Multilabel Text Classification Approach Using Genetic Algorithm and Ensemble Learning

An Optimized Arabic Multilabel Text Classification Approach Using Genetic Algorithm and Ensemble Learning

Attention-Induced Embedding Imputation for Incomplete Multi-View Partial Multi-Label Classification

Multi-label learning of missing labels using label-specific features: an embedded packaging method

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