18 citations · 32 across the 14 of their papers we have counts for
6 papers · 1 filter
A Novel Pareto-optimal Ranking Method for Comparing Multi-objective Optimization Algorithms
Amin Ibrahim, Azam Asilian Bidgoli, Shahryar Rahnamayan +1
As the interest in multi- and many-objective optimization algorithms grows, the performance comparison of these algorithms becomes increasingly important. A large number of perform…
Large-scale Multi-objective Feature Selection: A Multi-phase Search Space Shrinking Approach
Azam Asilian Bidgoli, Shahryar Rahnamayan
Feature selection is a crucial step in machine learning, especially for high-dimensional datasets, where irrelevant and redundant features can degrade model performance and increas…
Enhancing Diversity in Multi-objective Feature Selection
Sevil Zanjani Miyandoab, Shahryar Rahnamayan, Azam Asilian Bidgoli +2
Feature selection plays a pivotal role in the data preprocessing and model-building pipeline, significantly enhancing model performance, interpretability, and resource efficiency a…
Training Artificial Neural Networks by Coordinate Search Algorithm
Ehsan Rokhsatyazdi, Shahryar Rahnamayan, Sevil Zanjani Miyandoab +2
Training Artificial Neural Networks poses a challenging and critical problem in machine learning. Despite the effectiveness of gradient-based learning methods, such as Stochastic G…
Compact NSGA-II for Multi-objective Feature Selection
Sevil Zanjani Miyandoab, Shahryar Rahnamayan, Azam Asilian Bidgoli
Feature selection is an expensive challenging task in machine learning and data mining aimed at removing irrelevant and redundant features. This contributes to an improvement in cl…
Multi-objective Binary Coordinate Search for Feature Selection
Sevil Zanjani Miyandoab, Shahryar Rahnamayan, Azam Asilian Bidgoli
A supervised feature selection method selects an appropriate but concise set of features to differentiate classes, which is highly expensive for large-scale datasets. Therefore, fe…