Multiclass feature selection with metaheuristic optimization algorithms: a review
Selecting relevant feature subsets is vital in machine learning, and multiclass feature
selection is harder to perform since most classifications are binary. The feature selection …
selection is harder to perform since most classifications are binary. The feature selection …
A binary waterwheel plant optimization algorithm for feature selection
The vast majority of today's data is collected and stored in enormous databases with a wide
range of characteristics that have little to do with the overarching goal concept. Feature …
range of characteristics that have little to do with the overarching goal concept. Feature …
A hybrid binary dwarf mongoose optimization algorithm with simulated annealing for feature selection on high dimensional multi-class datasets
The dwarf mongoose optimization (DMO) algorithm developed in 2022 was applied to solve
continuous mechanical engineering design problems with a considerable balance of the …
continuous mechanical engineering design problems with a considerable balance of the …
Hybrid algorithms based on combining reinforcement learning and metaheuristic methods to solve global optimization problems
A Seyyedabbasi, R Aliyev, F Kiani, MU Gulle… - Knowledge-Based …, 2021 - Elsevier
This paper introduces three hybrid algorithms that help in solving global optimization
problems using reinforcement learning along with metaheuristic methods. Using the …
problems using reinforcement learning along with metaheuristic methods. Using the …
Design of multimodal hub-and-spoke transportation network for emergency relief under COVID-19 pandemic: A meta-heuristic approach
C Li, P Han, M Zhou, M Gu - Applied Soft Computing, 2023 - Elsevier
When COVID-19 suddenly broke out, the epidemic areas are short of basic emergency relief
which need to be transported from surrounding areas. To make transportation both time …
which need to be transported from surrounding areas. To make transportation both time …
Binary dwarf mongoose optimizer for solving high-dimensional feature selection problems
Selecting appropriate feature subsets is a vital task in machine learning. Its main goal is to
remove noisy, irrelevant, and redundant feature subsets that could negatively impact the …
remove noisy, irrelevant, and redundant feature subsets that could negatively impact the …
Privacy and efficiency guaranteed social subgraph matching
Due to the increasing cost of data storage and computation, more and more graphs (eg, web
graphs, social networks) are outsourced and analyzed in the cloud. However, there is …
graphs, social networks) are outsourced and analyzed in the cloud. However, there is …
Binary Ebola optimization search algorithm for feature selection and classification problems
In the past decade, the extraction of valuable information from online biomedical datasets
has exponentially increased due to the evolution of data processing devices and the …
has exponentially increased due to the evolution of data processing devices and the …
A heuristic aided Stochastic Beam Search algorithm for solving the transit network design problem
Designing efficient routes for a transit network is one of the main problems faced by city
planners of the world. Due to the largeness and complexity of modern day road networks it is …
planners of the world. Due to the largeness and complexity of modern day road networks it is …
Kids Learning Optimizer: social evolution and cognitive learning-based optimization algorithm
This paper proposes a novel social cognitive learning-based metaheuristic called kids
Learning Optimizer (KLO), inspired by the early social learning behavior of kids organized …
Learning Optimizer (KLO), inspired by the early social learning behavior of kids organized …