Smart city construction and management by digital twins and BIM big data in COVID-19 scenario
Z Lv, D Chen, H Lv - ACM Transactions on Multimedia Computing …, 2022 - dl.acm.org
With the rapid development of information technology and the spread of Corona Virus
Disease 2019 (COVID-19), the government and urban managers are looking for ways to use …
Disease 2019 (COVID-19), the government and urban managers are looking for ways to use …
Manifold regularized discriminative feature selection for multi-label learning
In multi-label learning, objects are essentially related to multiple semantic meanings, and
the type of data is confronted with the impact of high feature dimensionality simultaneously …
the type of data is confronted with the impact of high feature dimensionality simultaneously …
Information fusion for edge intelligence: A survey
Edge intelligence capability is expected to enable the development of a new paradigm
integrated with edge computing and artificial intelligence. However, due to the multisource …
integrated with edge computing and artificial intelligence. However, due to the multisource …
Joint imbalanced classification and feature selection for hospital readmissions
Hospital readmission is one of the most important service quality measures. Recently,
numerous risk assessment models have been proposed to address the hospital readmission …
numerous risk assessment models have been proposed to address the hospital readmission …
MULFE: multi-label learning via label-specific feature space ensemble
In multi-label learning, label correlations commonly exist in the data. Such correlation not
only provides useful information, but also imposes significant challenges for multi-label …
only provides useful information, but also imposes significant challenges for multi-label …
Multi-label feature selection based on correlation label enhancement
Z He, Y Lin, C Wang, L Guo, W Ding - Information Sciences, 2023 - Elsevier
Feature selection is an effective data preprocessing technique that can effectively alleviate
the curse of dimensionality in multi-label learning. The technique selects a subset of features …
the curse of dimensionality in multi-label learning. The technique selects a subset of features …
Non-aligned multi-view multi-label classification via learning view-specific labels
D Zhao, Q Gao, Y Lu, D Sun - IEEE Transactions on Multimedia, 2022 - ieeexplore.ieee.org
In the multi-view multi-label (MVML) classification problem, multiple views are
simultaneously associated with multiple semantic representations. Multi-view multi-label …
simultaneously associated with multiple semantic representations. Multi-view multi-label …
Consistency and diversity neural network multi-view multi-label learning
In multi-view multi-label learning, each object is represented by multiple heterogeneous
data and is simultaneously associated with multiple class labels. Previous studies usually …
data and is simultaneously associated with multiple class labels. Previous studies usually …
Feature selection for multi-label learning with streaming label
Multi-label feature selection has drawn wide attention in recent years. The existing multi-
label feature selection algorithms mainly assume that the labels of the training data are …
label feature selection algorithms mainly assume that the labels of the training data are …
Learning view-specific labels and label-feature dependence maximization for multi-view multi-label classification
D Zhao, Q Gao, Y Lu, D Sun - Applied Soft Computing, 2022 - Elsevier
Multi-view multi-label learning tasks often appear in various critical data classification
scenarios. Each training sample has multiple heterogeneous data views associated with …
scenarios. Each training sample has multiple heterogeneous data views associated with …