Evolutionary computation for feature selection in classification: A comprehensive survey of solutions, applications and challenges
Feature selection (FS), as one of the most significant preprocessing techniques in the fields
of machine learning and pattern recognition, has received great attention. In recent years …
of machine learning and pattern recognition, has received great attention. In recent years …
A comprehensive review of the imbalance classification of protein post-translational modifications
Post-translational modifications (PTMs) play significant roles in regulating protein structure,
activity and function, and they are closely involved in various pathologies. Therefore, the …
activity and function, and they are closely involved in various pathologies. Therefore, the …
Ensemble learning-based feature engineering to analyze maternal health during pregnancy and health risk prediction
Maternal health is an important aspect of women's health during pregnancy, childbirth, and
the postpartum period. Specifically, during pregnancy, different health factors like age, blood …
the postpartum period. Specifically, during pregnancy, different health factors like age, blood …
Automated endoscopic image classification via deep neural network with class imbalance loss
G Yue, P Wei, Y Liu, Y Luo, J Du… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Recently, many computer-aided diagnosis (CAD) methods have been proposed to help
physicians automatically classify endoscopic images. However, most existing methods often …
physicians automatically classify endoscopic images. However, most existing methods often …
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 …
TSK fuzzy system fusion at sensitivity-ensemble-level for imbalanced data classification
Previous studies have shown that the performance of a classifier on imbalanced data
heavily relies on informative objects lying in borderline or overlap** areas. In this study …
heavily relies on informative objects lying in borderline or overlap** areas. In this study …
Evolving ensembles using multi-objective genetic programming for imbalanced classification
Abstract Multi-objective Genetic Programming (MGP) plays a prominent role in generating
Pareto optimal classifier sets and making trade-offs among multiple classes adaptively …
Pareto optimal classifier sets and making trade-offs among multiple classes adaptively …
Target detection through tree-structured encoding for hyperspectral images
Target detection aims to locate targets of interest within a specific scene. The traditional
model-driven detectors based on signal processing have proved to be very effective …
model-driven detectors based on signal processing have proved to be very effective …
Evolutionary dual-ensemble class imbalance learning for human activity recognition
Y Guo, Y Chu, B Jiao, J Cheng, Z Yu… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Human activity recognition is an imbalance classification problem in essence since various
human actions may occur at different frequencies. Traditional ensemble class imbalance …
human actions may occur at different frequencies. Traditional ensemble class imbalance …
Ensemble-based information retrieval with mass estimation for hyperspectral target detection
Given the prior information of the target, hyperspectral target detection focuses on exploiting
spectral differences to separate objects of interest from the background, which can be …
spectral differences to separate objects of interest from the background, which can be …