Entropy and confidence-based undersampling boosting random forests for imbalanced problems
In this article, we propose a novel entropy and confidence-based undersampling boosting
(ECUBoost) framework to solve imbalanced problems. The boosting-based ensemble is …
(ECUBoost) framework to solve imbalanced problems. The boosting-based ensemble is …
Construction of EBRB classifier for imbalanced data based on Fuzzy C-Means clustering
YG Fu, JF Ye, ZF Yin, LJ Chen, YM Wang… - Knowledge-based …, 2021 - Elsevier
Abstract The Extended Belief Rule-Based (EBRB) system has been widely used to solve the
real-world problems concerning with incompleteness, uncertainty, and ambiguity. However …
real-world problems concerning with incompleteness, uncertainty, and ambiguity. However …
Semantic supplementary network with prior information for multi-label image classification
The multi-label image classification problem is one of the most important problems in the
field of computer vision, which needs to predict and output all the labels in an image …
field of computer vision, which needs to predict and output all the labels in an image …
FLDNet: Frame-level distilling neural network for EEG emotion recognition
Based on the current research on EEG emotion recognition, there are some limitations, such
as hand-engineered features, redundant and meaningless signal frames and the loss of …
as hand-engineered features, redundant and meaningless signal frames and the loss of …
EBRB cascade classifier for imbalanced data via rule weight updating
YG Fu, HY Huang, Y Guan, YM Wang, W Liu… - Knowledge-Based …, 2021 - Elsevier
In recent years, data imbalance in the conventional classification problem has raised great
interest in the industry. However, concerning the rule-based systems, this problem has been …
interest in the industry. However, concerning the rule-based systems, this problem has been …
Feature rearrangement based deep learning system for predicting heart failure mortality
Abstract Background and objective: Heart Failure is a clinical syndrome commonly caused
by any structural or functional impairment. Fast and accurate mortality prediction for Heart …
by any structural or functional impairment. Fast and accurate mortality prediction for Heart …
An empirical evaluation of stacked ensembles with different meta-learners in imbalanced classification
S Zian, SA Kareem, KD Varathan - IEEE Access, 2021 - ieeexplore.ieee.org
The selection of a meta-learner determines the success of a stacked ensemble as the meta-
learner is responsible for the final predictions of the stacked ensemble. Unfortunately, in …
learner is responsible for the final predictions of the stacked ensemble. Unfortunately, in …
A multimodal radiomic machine learning approach to predict the LCK expression and clinical prognosis in high-grade serous ovarian cancer
We developed and validated a multimodal radiomic machine learning approach to
noninvasively predict the expression of lymphocyte cell-specific protein-tyrosine kinase …
noninvasively predict the expression of lymphocyte cell-specific protein-tyrosine kinase …
Entropy‐based hybrid sampling ensemble learning for imbalanced data
L Dongdong, C Ziqiu, W Bolu, W Zhe… - … Journal of Intelligent …, 2021 - Wiley Online Library
Sampling method is one of the most commonly used techniques in dealing with imbalanced
data. Most of the existing undersampling methods randomly select samples from negative …
data. Most of the existing undersampling methods randomly select samples from negative …
Sample and feature selecting based ensemble learning for imbalanced problems
Imbalanced problem is concerned with the performance of classifiers on the data set with
severe class imbalance distribution. Traditional methods are misled by the majority samples …
severe class imbalance distribution. Traditional methods are misled by the majority samples …