A review of feature selection methods based on meta-heuristic algorithms
Feature selection is a real-world problem that finds a minimal feature subset from an original
feature set. A good feature selection method, in addition to selecting the most relevant …
feature set. A good feature selection method, in addition to selecting the most relevant …
PToPI: A comprehensive review, analysis, and knowledge representation of binary classification performance measures/metrics
Although few performance evaluation instruments have been used conventionally in
different machine learning-based classification problem domains, there are numerous ones …
different machine learning-based classification problem domains, there are numerous ones …
FW-SMOTE: A feature-weighted oversampling approach for imbalanced classification
Abstract The Synthetic Minority Over-sampling Technique (SMOTE) is a well-known
resampling strategy that has been successfully used for dealing with the class-imbalance …
resampling strategy that has been successfully used for dealing with the class-imbalance …
A surrogate-assisted evolutionary feature selection algorithm with parallel random grou** for high-dimensional classification
Various evolutionary algorithms (EAs) have been proposed to address feature selection (FS)
problems, in which a large number of fitness evaluations are needed. With the rapid growth …
problems, in which a large number of fitness evaluations are needed. With the rapid growth …
Improving deep learning classifiers performance via preprocessing and class imbalance approaches in a plant disease detection pipeline
The foundation of effectively predicting plant disease in the early stage using deep learning
algorithms is ideal for addressing food insecurity, inevitably drawing researchers and …
algorithms is ideal for addressing food insecurity, inevitably drawing researchers and …
On supervised class-imbalanced learning: An updated perspective and some key challenges
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 …
Transfer learning in human activity recognition: A survey
Sensor-based human activity recognition (HAR) has been an active research area, owing to
its applications in smart environments, assisted living, fitness, healthcare, etc. Recently …
its applications in smart environments, assisted living, fitness, healthcare, etc. Recently …
Bearing fault diagnosis based on combined multi-scale weighted entropy morphological filtering and bi-LSTM
F Zou, H Zhang, S Sang, X Li, W He, X Liu - Applied Intelligence, 2021 - Springer
With the development of industry and technology, mechanical systems' safety has strong
relations with the diagnosis of bearing faults. Accurate fault diagnosis is essential for the …
relations with the diagnosis of bearing faults. Accurate fault diagnosis is essential for the …
[HTML][HTML] Best practices for machine learning in antibody discovery and development
In the past 40 years, therapeutic antibody discovery and development have advanced
considerably, with machine learning (ML) offering a promising way to speed up the process …
considerably, with machine learning (ML) offering a promising way to speed up the process …
[HTML][HTML] Classification of diseases using machine learning algorithms: A comparative study
Machine learning in the medical area has become a very important requirement. The
healthcare professional needs useful tools to diagnose medical illnesses. Classifiers are …
healthcare professional needs useful tools to diagnose medical illnesses. Classifiers are …