[HTML][HTML] A review of ensemble learning and data augmentation models for class imbalanced problems: Combination, implementation and evaluation
Class imbalance (CI) in classification problems arises when the number of observations
belonging to one class is lower than the other. Ensemble learning combines multiple models …
belonging to one class is lower than the other. Ensemble learning combines multiple models …
A review on solution to class imbalance problem: Undersampling approaches
The classification task carries a significant role in the field of effective data mining and
numerous classification models are proposed over the years to carry out the job. However …
numerous classification models are proposed over the years to carry out the job. However …
Counterfactual inference for text classification debiasing
Today's text classifiers inevitably suffer from unintended dataset biases, especially the
document-level label bias and word-level keyword bias, which may hurt models' …
document-level label bias and word-level keyword bias, which may hurt models' …
A novel ensemble feature selection method by integrating multiple ranking information combined with an SVM ensemble model for enterprise credit risk prediction in …
G Yao, X Hu, G Wang - Expert Systems with Applications, 2022 - Elsevier
Enterprise credit risk prediction in the supply chain context is an important step for decision
making and early credit crisis warnings. Improving the prediction performance of this task is …
making and early credit crisis warnings. Improving the prediction performance of this task is …
RA-GCN: Graph convolutional network for disease prediction problems with imbalanced data
Disease prediction is a well-known classification problem in medical applications. Graph
Convolutional Networks (GCNs) provide a powerful tool for analyzing the patients' features …
Convolutional Networks (GCNs) provide a powerful tool for analyzing the patients' features …
CUS-heterogeneous ensemble-based financial distress prediction for imbalanced dataset with ensemble feature selection
X Du, W Li, S Ruan, L Li - Applied Soft Computing, 2020 - Elsevier
Due to the global financial crisis occurred in 2008, with a large amount of companies
troubling in financial distress, the machine learning-based prediction of this dilemma has …
troubling in financial distress, the machine learning-based prediction of this dilemma has …
An ensemble imbalanced classification method based on model dynamic selection driven by data partition hybrid sampling
X Gao, B Ren, H Zhang, B Sun, J Li, J Xu, Y He… - Expert Systems with …, 2020 - Elsevier
In many real-world applications classification problems suffer from class-imbalance. The
classification methods for imbalanced data with only data processing or algorithm …
classification methods for imbalanced data with only data processing or algorithm …
Should we rely on entity mentions for relation extraction? debiasing relation extraction with counterfactual analysis
Recent literature focuses on utilizing the entity information in the sentence-level relation
extraction (RE), but this risks leaking superficial and spurious clues of relations. As a result …
extraction (RE), but this risks leaking superficial and spurious clues of relations. As a result …
Data-driven based approach to aid Parkinson's disease diagnosis
This article presents a machine learning methodology for diagnosing Parkinson's disease
(PD) based on the use of vertical Ground Reaction Forces (vGRFs) data collected from the …
(PD) based on the use of vertical Ground Reaction Forces (vGRFs) data collected from the …
A weighted hybrid ensemble method for classifying imbalanced data
J Zhao, J Jin, S Chen, R Zhang, B Yu, Q Liu - Knowledge-based systems, 2020 - Elsevier
In real datasets, most are unbalanced. Data imbalance can be defined as the number of
instances in some classes greatly exceeds the number of instances in other classes …
instances in some classes greatly exceeds the number of instances in other classes …