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 …
Automatic design of machine learning via evolutionary computation: A survey
Abstract Machine learning (ML), as the most promising paradigm to discover deep
knowledge from data, has been widely applied to practical applications, such as …
knowledge from data, has been widely applied to practical applications, such as …
A survey on unbalanced classification: How can evolutionary computation help?
Unbalanced classification is an essential machine learning task, which has attracted
widespread attention from both the academic and industrial communities due mainly to its …
widespread attention from both the academic and industrial communities due mainly to its …
Retinal Fundus Multi-Disease Image Dataset (RFMiD) 2.0: a dataset of frequently and rarely identified diseases
Irreversible vision loss is a worldwide threat. Developing a computer-aided diagnosis
system to detect retinal fundus diseases is extremely useful and serviceable to …
system to detect retinal fundus diseases is extremely useful and serviceable to …
Bridging the gap between AI and healthcare sides: towards developing clinically relevant AI-powered diagnosis systems
Despite the success of Convolutional Neural Network-based Computer-Aided Diagnosis
research, its clinical applications remain challenging. Accordingly, developing medical …
research, its clinical applications remain challenging. Accordingly, developing medical …
PCCT: Progressive class-center triplet loss for imbalanced medical image classification
Imbalanced training data in medical image diagnosis is a significant challenge for
diagnosing rare diseases. For this purpose, we propose a novel two-stage Progressive …
diagnosing rare diseases. For this purpose, we propose a novel two-stage Progressive …
Combination of feature selection and resampling methods to predict preterm birth based on electrohysterographic signals from imbalance data
Due to its high sensitivity, electrohysterography (EHG) has emerged as an alternative
technique for predicting preterm labor. The main obstacle in designing preterm labor …
technique for predicting preterm labor. The main obstacle in designing preterm labor …
Deep reinforcement learning framework for thoracic diseases classification via prior knowledge guidance
The chest X-ray is commonly employed in the diagnosis of thoracic diseases. Over the
years, numerous approaches have been proposed to address the issue of automatic …
years, numerous approaches have been proposed to address the issue of automatic …
Deep learning and thresholding with class-imbalanced big data
JM Johnson, TM Khoshgoftaar - 2019 18th IEEE international …, 2019 - ieeexplore.ieee.org
Class imbalance is a regularly occurring problem in machine learning that has been studied
extensively over the last two decades. Various methods for addressing class imbalance …
extensively over the last two decades. Various methods for addressing class imbalance …
Addressing the class imbalance problem in medical image segmentation via accelerated tversky loss function
N Nasalwai, NS Punn, SK Sonbhadra… - Pacific-Asia conference on …, 2021 - Springer
Image segmentation in the medical domain has gained a lot of research interest in recent
years with the advancements in deep learning algorithms and related technologies. Medical …
years with the advancements in deep learning algorithms and related technologies. Medical …