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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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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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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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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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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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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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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...
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Multi-label feature selection via maximum dynamic correlation change and minimum label redundancy
Information-theoretic measures have been commonly applied to evaluate the relevance and redundancy in multi-label feature selection. However, the...
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Prototype selection for multi-label data based on label correlation
In multi-label learning, the training data is typically large-scale and contains numerous noisy and redundant instances. Directly inducing a...
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Joint subspace reconstruction and label correlation for multi-label feature selection
High-dimensional multi-label data has become more prevalent in many application domains, presenting difficulties and challenges for multi-label...
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Multi-label learning with Relief-based label-specific feature selection
Multi-label learning is an emerging paradigm exploiting samples with rich semantics. As an effective solution to multi-label learning, the strategy...
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Neural Clustering and Ranking Approach for Gas-Theft Suspect Detection
Some boiler room users steal natural gas by refitting equipment without permission in winter, resulting in gas safety hazards and social problems....
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Deep Hash Learning of Feature-Invariant Representation for Single-Label and Multi-label Retrieval
In large-scale retrieval, hash learning is favored by people owing to its fast speed. Nowadays, many hashing methods based on deep learning are...
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Personalized Re-ranking for Recommendation with Mask Pretraining
Re-ranking is to refine the candidate ranking list of recommended items, such that the re-ranked list attracts users to purchase or click more items...
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Encoding laparoscopic image to words using vision transformer for distortion classification and ranking in laparoscopic videos
Laparoscopic videos are tools used by surgeons to insert narrow tubes into the abdomen and keep the skin without large incisions. The videos captured...
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Multi-label learning of missing labels using label-specific features: an embedded packaging method
Learning label-specific features is an effective strategy for multi-label classification. Existing multi-label classification methods for learning...
