Search
Search Results
-
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...
-
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...
-
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...
-
Set-based visualization and enhancement of embedding results for heterogeneous multi-label networks
Heterogeneous networks are ubiquitous in the real-world, such as social networks and brain cell networks. Network embedding techniques have emerged...
-
Label enhancement with label-specific feature learning
Label distribution learning (LDL) is a novel machine learning paradigm. It addresses the problem of label ambiguity by emphasizing the relevance of...
-
LEFSA: label enhancement-based feature selection with adaptive neighborhood via ant colony optimization for multilabel learning
To date, multilabel learning has garnered attention increased from scholars and has a significant effect on practical applications; however, most...
-
Semi-supervised label enhancement via structured semantic extraction
Label enhancement (LE) is a process of recovering the label distribution from logical labels in the datasets, the goal of which is to better express...
-
Label-dependent feature exploration for label distribution learning
Label distribution learning (LDL) explicitly models label ambiguity by assigning a real-valued vector with label description degrees to each sample....
-
Multi-granular labels with three-way decisions for multi-label classification
Multi-label classification is a challenging issue because it simultaneously embraces the characteristics of the imbalanced class distribution for...
-
A Chinese named entity recognition model: integrating label knowledge and lexicon information
Chinese named entity recognition (CNER) is one of the important tasks in the field of information extraction. And different divisions of CNER for...
-
Event detection algorithm based on label semantic encoding
One major challenge in event detection tasks is the lack of a large amount of annotated data. In a low-sample learning environment, effectively...
-
GCN-ResNet: A Multi-label Classifier for ECG Arrhythmia
Automatic ECG classification using artificial intelligence technology is of great significance for the early prevention and diagnosis of...
-
Feature selection for label distribution learning under feature weight view
Label Distribution Learning (LDL) is a fine-grained learning paradigm that addresses label ambiguity, yet it confronts the curse of dimensionality....
-
Classification of intelligent speech system and education method based on improved multi label transfer learning model
In recent years, improved multi label learning has been widely used in text classification, protein function prediction, image annotation and other...
-
Boosting Unsupervised Domain Adaptation with Soft Pseudo-Label and Curriculum Learning
By leveraging data from a fully labeled source domain, unsupervised domain adaptation (UDA) improves classification performance on an unlabeled...
-
A novel framework for multi-label feature selection: integrating mutual information and Pythagorean fuzzy CRADIS
In recent years, there has been a growing interest in multi-label data classification, with a particular emphasis on multi-label feature selection....
-
Improving text classification via a soft dynamical label strategy
Labels play a central role in the text classification tasks. However, most studies has a lossy label encoding problem, in which the label will be...
-
An oversampling algorithm of multi-label data based on cluster-specific samples and fuzzy rough set theory
Imbalanced class distributions are common in real-world scenarios, including datasets with multiple labels. One widely acknowledged approach to...
-
Global Dense Two-Branch Cascade Network for Underwater Image Enhancement
In recent years, underwater image enhancement techniques has received a wide range of attention from related researchers with the rise of marine...
-
Active label-denoising algorithm based on broad learning for annotation of machine health status
Deep learning has led to tremendous success in machine maintenance and fault diagnosis. However, this success is predicated on the correctly...