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Multi-Label Ranking: Mining Multi-Label and Label Ranking Data
We survey multi-label ranking tasks, specifically multi-label classification and label ranking classification. We highlight the unique challenges,...
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Ranking-preserved generative label enhancement
Label distribution learning (LDL) is effective for addressing label ambiguity. In LDL, ground-truth label distributions are hardly available due to...
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Partial Calibrated Multi-label Ranking
An approach to solve Multi-Label Classification (MLC) is to transform this task into a label ranking problem. An example of this is the Calibrated...
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An in-depth analysis of passage-level label transfer for contextual document ranking
Pre-trained contextual language models such as BERT, GPT, and XLnet work quite well for document retrieval tasks. Such models are fine-tuned based on...
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An Imprecise Label Ranking Method for Heterogeneous Data
Learning to rank is an important problem in many sectors ranging from social sciences to artificial intelligence. However, it remains a rather...
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Fuzzy information gain ratio-based multi-label feature selection with label correlation
Multi-label feature selection aims to mitigate the curse of dimensionality in multi-label data by selecting a smaller subset of features from the...
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Automatic diagnosis of myopic maculopathy using continuous severity ranking labels
Clinical lesions progress continuously but previous grading strategies are not fine-grained enough to model the continuously changing features of...
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Dual-channel graph contrastive learning for multi-label classification with label-specific features and label correlations
In multi-label classification scenarios, the labels have both interactive correlations and their own respective characteristics. It is a meaningful...
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Dynamic multi-label feature selection algorithm based on label importance and label correlation
Multi-label distribution is a popular direction in current machine learning research and is relevant to many practical problems. In multi-label...
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Multi-label classification with weak labels by learning label correlation and label regularization
In conventional multi-label learning, each training instance is associated with multiple available labels. Nevertheless, real-world objects usually...
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LDW-RS Loss: Label Density-Weighted Loss with Ranking Similarity Regularization for Imbalanced Deep Fetal Brain Age Regression
Estimation of fetal brain age based on sulci by magnetic resonance imaging (MRI) is crucial in determining the normal development of the fetal brain....
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Multi-label feature selection via joint label enhancement and pairwise label correlations
Multi-label feature selection(MFS) has gained in importance, and it is today confronted with the current need to process multi-semantic...
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Natural-neighborhood based, label-specific undersampling for imbalanced, multi-label data
This work presents a novel undersampling scheme to tackle the imbalance problem in multi-label datasets. We use the principles of the natural nearest...
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A Core-Partition Ranking Solution to Coalitional Ranking Problems
A coalitional ranking problem is described by a weak order on the set of nonempty coalitions of a given agent set. A social ranking is a weak order...
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Novel multi-label feature selection via label enhancement and relative maximal discernibility pairs
Multi-label feature selection is an effective solution to the multi-label data dimensionality disaster problem. However, there are few studies on...
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Multi-label feature selection via spectral clustering-based label enhancement and manifold distribution consistency
Multi-label feature selection can effectively improve the performance and efficiency of subsequent learning tasks by selecting important features...
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Ranking Enhanced Supervised Contrastive Learning for Regression
Supervised contrastive learning has shown promising results in image classification tasks where the representations are pulled together if they share...
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TLC-XML: Transformer with Label Correlation for Extreme Multi-label Text Classification
Extreme multi-label text classification (XMTC) annotates related labels for unknown text from large-scale label sets. Transformer-based methods have...
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Enhancing Detailed Feedback to Chinese Writing Learners Using a Soft-Label Driven Approach and Tag-Aware Ranking Model
This paper focuses on providing detailed and specific feedback for Chinese writing learners, which is challenging due to the uncertainty of the...
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Granular ball-based label enhancement for dimensionality reduction in multi-label data
AbstractAs an important preprocessing procedure, dimensionality reduction for multi-label learning is an effective way to solve the challenge caused...
