Metaheuristic design of feedforward neural networks: A review of two decades of research
Over the past two decades, the feedforward neural network (FNN) optimization has been a
key interest among the researchers and practitioners of multiple disciplines. The FNN …
key interest among the researchers and practitioners of multiple disciplines. The FNN …
Learnheuristics: hybridizing metaheuristics with machine learning for optimization with dynamic inputs
This paper reviews the existing literature on the combination of metaheuristics with machine
learning methods and then introduces the concept of learnheuristics, a novel type of hybrid …
learning methods and then introduces the concept of learnheuristics, a novel type of hybrid …
Information-theory-based nondominated sorting ant colony optimization for multiobjective feature selection in classification
Feature selection (FS) has received significant attention since the use of a well-selected
subset of features may achieve better classification performance than that of full features in …
subset of features may achieve better classification performance than that of full features in …
Co-learning feature fusion maps from PET-CT images of lung cancer
The analysis of multi-modality positron emission tomography and computed tomography
(PET-CT) images for computer-aided diagnosis applications (eg, detection and …
(PET-CT) images for computer-aided diagnosis applications (eg, detection and …
Diversified sensitivity-based undersampling for imbalance classification problems
Undersampling is a widely adopted method to deal with imbalance pattern classification
problems. Current methods mainly depend on either random resampling on the majority …
problems. Current methods mainly depend on either random resampling on the majority …
Neural network classifier optimization using differential evolution with global information and back propagation algorithm for clinical datasets
Abstract A Computer-Aided Diagnostic (CAD) system that uses Artificial Neural Network
(ANN) trained by drawing in the relative advantages of Differential Evolution (DE), Particle …
(ANN) trained by drawing in the relative advantages of Differential Evolution (DE), Particle …
A review of classification problems and algorithms in renewable energy applications
Classification problems and their corresponding solving approaches constitute one of the
fields of machine learning. The application of classification schemes in Renewable Energy …
fields of machine learning. The application of classification schemes in Renewable Energy …
RAMOBoost: Ranked minority oversampling in boosting
In recent years, learning from imbalanced data has attracted growing attention from both
academia and industry due to the explosive growth of applications that use and produce …
academia and industry due to the explosive growth of applications that use and produce …
Symmetric uncertainty-incorporated probabilistic sequence-based ant colony optimization for feature selection in classification
Feature selection (FS), which aims to select informative feature subsets and improve
classification performance, is a crucial data-mining technique. Recently, swarm intelligence …
classification performance, is a crucial data-mining technique. Recently, swarm intelligence …
Convolutional neural networks for recognition of lymphoblast cell images
T Pansombut, S Wikaisuksakul… - Computational …, 2019 - Wiley Online Library
This paper presents the recognition for WHO classification of acute lymphoblastic leukaemia
(ALL) subtypes. The two ALL subtypes considered are T‐lymphoblastic leukaemia (pre‐T) …
(ALL) subtypes. The two ALL subtypes considered are T‐lymphoblastic leukaemia (pre‐T) …