[HTML][HTML] A comprehensive data level analysis for cancer diagnosis on imbalanced data
The early diagnosis of cancer, as one of the major causes of death, is vital for cancerous
patients. Diagnosing diseases in general and cancer in particular is a considerable …
patients. Diagnosing diseases in general and cancer in particular is a considerable …
[HTML][HTML] Random forest swarm optimization-based for heart diseases diagnosis
Heart disease has been one of the leading causes of death worldwide in recent years.
Among diagnostic methods for heart disease, angiography is one of the most common …
Among diagnostic methods for heart disease, angiography is one of the most common …
Improvement of Bagging performance for classification of imbalanced datasets using evolutionary multi-objective optimization
Today, classification of imbalanced datasets, in which the samples belonging to one class is
more than the samples pertaining to other classes, has been paid much attention owing to …
more than the samples pertaining to other classes, has been paid much attention owing to …
On supervised class-imbalanced learning: An updated perspective and some key challenges
S Das, SS Mullick, I Zelinka - IEEE Transactions on Artificial …, 2022 - ieeexplore.ieee.org
The problem of class imbalance has always been considered as a significant challenge to
traditional machine learning and the emerging deep learning research communities. A …
traditional machine learning and the emerging deep learning research communities. A …
Daily water level prediction of Zrebar Lake (Iran): a comparison between M5P, random forest, random tree and reduced error pruning trees algorithms
Zrebar Lake is one of the largest freshwater lakes in Iran and it plays an important role in the
ecosystem of the environment, while its desiccation has a negative impact on the …
ecosystem of the environment, while its desiccation has a negative impact on the …
An evolutionary deep belief network extreme learning-based for breast cancer diagnosis
S Ronoud, S Asadi - Soft Computing, 2019 - Springer
Cancer is one of the leading causes of morbidity and mortality worldwide with increasing
prevalence. Breast cancer is the most common type among women, and its early diagnosis …
prevalence. Breast cancer is the most common type among women, and its early diagnosis …
Multi-objective boxing match algorithm for multi-objective optimization problems
R Tavakkoli-Moghaddam, AH Akbari… - Expert Systems with …, 2024 - Elsevier
In the last two decades, due to having fast computation after inventing computers and also
considering real-world optimization problems, research on developing new algorithms for …
considering real-world optimization problems, research on developing new algorithms for …
A novel ensemble learning based on Bayesian Belief Network coupled with an extreme learning machine for flash flood susceptibility mapping
Reliable flash flood susceptibility maps are a vital tool for land planners and emergency
management officials for early flood warning and mitigation. We have developed a new …
management officials for early flood warning and mitigation. We have developed a new …
A bi-objective optimization method to produce a near-optimal number of classifiers and increase diversity in Bagging
Bagging is an old and powerful method in ensemble learning which creates an ensemble of
classifiers over bootstraps through learning and then generates diverse classifiers. There …
classifiers over bootstraps through learning and then generates diverse classifiers. There …
Diverse training dataset generation based on a multi-objective optimization for semi-supervised classification
The self-labeled technique is a type of semi-supervised classification that can be used when
labeled data are lacking. Although existing self-labeled techniques show promise in many …
labeled data are lacking. Although existing self-labeled techniques show promise in many …