A review of methods for imbalanced multi-label classification
Abstract Multi-Label Classification (MLC) is an extension of the standard single-label
classification where each data instance is associated with several labels simultaneously …
classification where each data instance is associated with several labels simultaneously …
[PDF][PDF] EEG-based emotion recognition using 3D convolutional neural networks
ES Salama, RA El-Khoribi… - International …, 2018 - pdfs.semanticscholar.org
Human-Computer Interaction (HCI). Various techniques were applied to enhance the
robustness of the emotion recognition systems using electroencephalogram (EEG) signals …
robustness of the emotion recognition systems using electroencephalogram (EEG) signals …
Deep learning and Internet of Things for tourist attraction recommendations in smart cities
JC Cepeda-Pacheco, MC Domingo - Neural Computing and Applications, 2022 - Springer
We propose a tourist attraction IoT-enabled deep learning-based recommendation system to
enhance tourist experience in a smart city. Travelers will enter details about their travels …
enhance tourist experience in a smart city. Travelers will enter details about their travels …
Application of artificial intelligence approaches to predict the metabolism of xenobiotic molecules by human gut microbiome
A highly complex, diverse, and dense community of more than 1,000 different gut bacterial
species constitutes the human gut microbiome that harbours vast metabolic capabilities …
species constitutes the human gut microbiome that harbours vast metabolic capabilities …
Learning to learn and predict: A meta-learning approach for multi-label classification
Many tasks in natural language processing can be viewed as multi-label classification
problems. However, most of the existing models are trained with the standard cross-entropy …
problems. However, most of the existing models are trained with the standard cross-entropy …
A multi-label, semi-supervised classification approach applied to personality prediction in social media
ACES Lima, LN De Castro - Neural Networks, 2014 - Elsevier
Social media allow web users to create and share content pertaining to different subjects,
exposing their activities, opinions, feelings and thoughts. In this context, online social media …
exposing their activities, opinions, feelings and thoughts. In this context, online social media …
[PDF][PDF] Correlation-based pruning of stacked binary relevance models for multi-label learning
Binary relevance (BR) learns a single binary model for each different label of multi-label
data. It has linear complexity with respect to the number of labels, but does not take into …
data. It has linear complexity with respect to the number of labels, but does not take into …
An extensive survey on the use of supervised machine learning techniques in the past two decades for prediction of drug side effects
P Das, DH Mazumder - Artificial Intelligence Review, 2023 - Springer
Approved drugs for sale must be effective and safe, implying that the drug's advantages
outweigh its known harmful side effects. Side effects (SE) of drugs are one of the common …
outweigh its known harmful side effects. Side effects (SE) of drugs are one of the common …
Is the latest the greatest? A comparative study of automatic approaches for classifying educational forum posts
In online courses, discussion forums play a key role in enhancing student interaction with
peers and instructors. Due to large enrolment sizes, instructors often struggle to respond to …
peers and instructors. Due to large enrolment sizes, instructors often struggle to respond to …
Robust bloom filters for large multilabel classification tasks
This paper presents an approach to multilabel classification (MLC) with a large number of
labels. Our approach is a reduction to binary classification in which label sets are …
labels. Our approach is a reduction to binary classification in which label sets are …