AdaBoost-CNN: An adaptive boosting algorithm for convolutional neural networks to classify multi-class imbalanced datasets using transfer learning

A Taherkhani, G Cosma, TM McGinnity - Neurocomputing, 2020 - Elsevier
Ensemble models achieve high accuracy by combining a number of base estimators and
can increase the reliability of machine learning compared to a single estimator. Additionally …

Fuzzy clustering method to compare the spread rate of Covid-19 in the high risks countries

MR Mahmoudi, D Baleanu, Z Mansor, BA Tuan… - Chaos, Solitons & …, 2020 - Elsevier
The numbers of confirmed cases of new coronavirus (Covid-19) are increased daily in
different countries. To determine the policies and plans, the study of the relations between …

Class imbalance ensemble learning based on the margin theory

W Feng, W Huang, J Ren - Applied Sciences, 2018 - mdpi.com
The proportion of instances belonging to each class in a data-set plays an important role in
machine learning. However, the real world data often suffer from class imbalance. Dealing …

[HTML][HTML] Simultaneous design of fuzzy PSS and fuzzy STATCOM controllers for power system stability enhancement

J Ansari, AR Abbasi, MH Heydari… - Alexandria Engineering …, 2022 - Elsevier
The low frequency oscillations have always been the main problem of power system and
can lead to power angle instability, limiting the maximum power to be transmitted on tie-lines …

[HTML][HTML] A hybrid deep neural net learning model for predicting Coronary Heart Disease using Randomized Search Cross-Validation Optimization

N Sharma, L Malviya, A Jadhav, P Lalwani - Decision Analytics Journal, 2023 - Elsevier
Abstract Coronary Heart Disease (CHD) is a life-threatening public health problem. Many
chronic CHDs and health risks can be avoided, reversed, and reduced with proper risk …

Spatial distribution-based imbalanced undersampling

Y Yan, Y Zhu, R Liu, Y Zhang, Y Zhang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Undersampling is one of the most popular techniques for dealing with class-imbalance
problems. Various undersampling methods have emerged over the past few decades. Each …

Adaptive clustering-based malicious traffic classification at the network edge

AF Diallo, P Patras - IEEE INFOCOM 2021-IEEE Conference …, 2021 - ieeexplore.ieee.org
The rapid uptake of digital services and Internet of Things (IoT) technology gives rise to
unprecedented numbers and diversification of cyber attacks, with which commonly-used rule …

Tomato disease and pest diagnosis method based on the Stacking of prescription data

C Xu, J Ding, Y Qiao, L Zhang - Computers and Electronics in Agriculture, 2022 - Elsevier
Crop prescription data contains an extensive amount of information on crops, environment
and pests, and has notable diagnostic capabilities. At present, there is lack of feasible …

Adaptive subspace optimization ensemble method for high-dimensional imbalanced data classification

Y Xu, Z Yu, CLP Chen, Z Liu - IEEE Transactions on Neural …, 2021 - ieeexplore.ieee.org
It is hard to construct an optimal classifier for high-dimensional imbalanced data, on which
the performance of classifiers is seriously affected and becomes poor. Although many …

A comprehensive study of clustering ensemble weighting based on cluster quality and diversity

A Nazari, A Dehghan, S Nejatian, V Rezaie… - Pattern Analysis and …, 2019 - Springer
Clustering as a major task in data mining is responsible for discovering hidden patterns in
unlabeled datasets. Finding the best clustering is also considered as one of the most …