Robust Label and Feature Space Co-Learning for Multi-Label Classification

Liu, Zhifeng; Tang, Chuanjing; Abhadiomhen, Stanley Ebhohimhen; Shen, Xiang-Jun; Li, Yangyang

doi:10.1109/tkde.2022.3232114

Cited by 8 publications

(5 citation statements)

References 42 publications

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“…The above theorem reveals the equivalence between (19) and (20), under some circumstances in (21). A summary of conclusions of Theorem 1 is shown in Table 2.…”

Section: Generalized Estf and Theoretical Analysismentioning

confidence: 72%

“…For single-view MLC experiments, we compare the proposed MLPC in the CP, TT and TR formats with three types of methods: MLC, PL and baseline. For MLC, we compare MLPC with state-ofthe-art methods, including LLSF [10], GRRO [49], RLFSCL [20] and WRAP [44]. Both LLSF and WRAP learn label-specific features, while GRRO considers feature relevance, feature redundancy and label correlation simultaneously.…”

Section: Compared Methodsmentioning

confidence: 99%

“…DEFRAG [12] performs hierarchical clustering on representative vectors to obtain feature clusters. RLFSCL [20] projects features and labels into a common latent space and regularizes the nuclear norm to achieve a low-rank model.…”

Section: Multi-label Classificationmentioning

confidence: 99%

“…For L(•, •), there are several possible choices, such as squared loss, cross entropy loss and hinge loss. For Ω(•), various regularizations have been used for MLC, such as the Frobenius norm [35], the nuclear norm [20] and the l 2,1 -norm [22].…”

Section: Multi-label Classificationmentioning

confidence: 99%

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Multi-View Multi-Label Personalized Classification via Generalized Exclusive Sparse Tensor Factorization

Fei,

Lin,

Wang

et al. 2024

Preprint

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Multi-Label Classification (MLC) assigns multiple relevant labels to each sample simultaneously,while multi-view MLC aims to apply MLC to handle heterogeneous data represented by multiple feature subsets.In recent years, a variety of methods have been proposed to handle these problems and have achieved great success in a wide range of applications. MLC saves global label correlation by building a single model shared by all samples but ignores sample-specific local structures, while Personalized Learning (PL) is able to preserve sample-specific information by learning local models but ignores the global structure. Integrating PL with MLC is a straightforward way to overcome the limitations, but it still faces three key challenges. 1) capture both local and global structures in a unified model; 2) efficiently preserve full-order interactions between labels, samples and features or multi-view features in heterogeneous data; 3) learn a concise and interpretable model where only a fraction of interactions are associated with multiple labels. In this paper, we propose a novel Multi-Label Personalized Classification (MLPC) method and its multi-view extension to handle these challenges. For 1), it integrates local and global components to preserve sample-specific information and global structure shared across samples, respectively. For 2), a multilinear model is developed to capture full-order label-feature-sample interactions, and over-parameterization is avoided by tensor factorization. For 3), exclusive sparsity regularization penalizes factorization by promoting intra-group competition, thereby eliminating irrelevant and redundant interactions during Exclusive Sparse Tensor Factorization (ESTF). Moreover, theoretical analysis generalizes the proposed ESTF and reveals the equivalence between MLPC and a family of jointly regularized counterparts. We develop an alternating algorithm to solve the optimization problem, and demonstrate its effectiveness based on comprehensive experiments on both synthetic and real-world benchmark datasets.

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“…The above theorem reveals the equivalence between (19) and (20), under some circumstances in (21). A summary of conclusions of Theorem 1 is shown in Table 2.…”

Section: Generalized Estf and Theoretical Analysismentioning

confidence: 72%

Section: Compared Methodsmentioning

confidence: 99%

Section: Multi-label Classificationmentioning

confidence: 99%

Section: Multi-label Classificationmentioning

confidence: 99%

See 2 more Smart Citations

Multi-View Multi-Label Personalized Classification via Generalized Exclusive Sparse Tensor Factorization

Fei,

Lin,

Wang

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Additionally, classifiers that can develop a global model for a class perform worse when multi-label datasets are classified [ 5 ]. To concentrate on the decision limits of the classifier(s) in each area, clustering has previously been used to extract the distribution of data completely or separately for each class [ 6 , 7 ]. In other words, one classifier is trained for each cluster of data after the initial clustering of the data.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Multi-Label Classification Model for Medical Applications Based on Adaptive Synthetic Data and Ensemble Learning

Priyadharshini,

Banu,

Sharma

et al. 2023

Sensors

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In recent years, both machine learning and computer vision have seen growth in the use of multi-label categorization. SMOTE is now being utilized in existing research for data balance, and SMOTE does not consider that nearby examples may be from different classes when producing synthetic samples. As a result, there can be more class overlap and more noise. To avoid this problem, this work presented an innovative technique called Adaptive Synthetic Data-Based Multi-label Classification (ASDMLC). Adaptive Synthetic (ADASYN) sampling is a sampling strategy for learning from unbalanced data sets. ADASYN weights minority class instances by learning difficulty. For hard-to-learn minority class cases, synthetic data are created. Their numerical variables are normalized with the help of the Min-Max technique to standardize the magnitude of each variable’s impact on the outcomes. The values of the attribute in this work are changed to a new range, from 0 to 1, using the normalization approach. To raise the accuracy of multi-label classification, Velocity-Equalized Particle Swarm Optimization (VPSO) is utilized for feature selection. In the proposed approach, to overcome the premature convergence problem, standard PSO has been improved by equalizing the velocity with each dimension of the problem. To expose the inherent label dependencies, the multi-label classification ensemble of Adaptive Neuro-Fuzzy Inference System (ANFIS), Probabilistic Neural Network (PNN), and Clustering-Based Decision tree methods will be processed based on an averaging method. The following criteria, including precision, recall, accuracy, and error rate, are used to assess performance. The suggested model’s multi-label classification accuracy is 90.88%, better than previous techniques, which is PCT, HOMER, and ML-Forest is 65.57%, 70.66%, and 82.29%, respectively.

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