[HTML][HTML] Relief-based feature selection: Introduction and review
Feature selection plays a critical role in biomedical data mining, driven by increasing feature
dimensionality in target problems and growing interest in advanced but computationally …
dimensionality in target problems and growing interest in advanced but computationally …
A comprehensive survey on the process, methods, evaluation, and challenges of feature selection
Feature selection is employed to reduce the feature dimensions and computational
complexity by eliminating irrelevant and redundant features. A vast amount of increasing …
complexity by eliminating irrelevant and redundant features. A vast amount of increasing …
Designing a feature selection method based on explainable artificial intelligence
J Zacharias, M von Zahn, J Chen, O Hinz - Electronic Markets, 2022 - Springer
Nowadays, artificial intelligence (AI) systems make predictions in numerous high stakes
domains, including credit-risk assessment and medical diagnostics. Consequently, AI …
domains, including credit-risk assessment and medical diagnostics. Consequently, AI …
MFS-MCDM: Multi-label feature selection using multi-criteria decision making
A Hashemi, MB Dowlatshahi… - Knowledge-Based …, 2020 - Elsevier
In this paper, for the first time, a feature selection procedure is modeled as a multi-criteria
decision making (MCDM) process. This method is applied to a multi-label data and we have …
decision making (MCDM) process. This method is applied to a multi-label data and we have …
Ensemble of feature selection algorithms: a multi-criteria decision-making approach
A Hashemi, MB Dowlatshahi… - International Journal of …, 2022 - Springer
For the first time, the ensemble feature selection is modeled as a Multi-Criteria Decision-
Making (MCDM) process in this paper. For this purpose, we used the VIKOR method as a …
Making (MCDM) process in this paper. For this purpose, we used the VIKOR method as a …
[HTML][HTML] Benchmarking relief-based feature selection methods for bioinformatics data mining
Modern biomedical data mining requires feature selection methods that can (1) be applied
to large scale feature spaces (eg 'omics' data),(2) function in noisy problems,(3) detect …
to large scale feature spaces (eg 'omics' data),(2) function in noisy problems,(3) detect …
Ant colony optimization equipped with an ensemble of heuristics through multi-criteria decision making: A case study in ensemble feature selection
Abstract Ant Colony Optimization (ACO) is a probabilistic and approximation metaheuristic
algorithm to solve complex combinatorial optimization problems. ACO algorithm is inspired …
algorithm to solve complex combinatorial optimization problems. ACO algorithm is inspired …
A pareto-based ensemble of feature selection algorithms
A Hashemi, MB Dowlatshahi… - Expert Systems with …, 2021 - Elsevier
In this paper, ensemble feature selection is modeled as a bi-objective optimization problem
regarding features' relevancy and redundancy degree. The proposed method, which is …
regarding features' relevancy and redundancy degree. The proposed method, which is …
Regret theory-based multivariate fusion prediction system and its application to interest rate estimation in multi-scale information systems
X Huang, J Zhan, W Ding, W Pedrycz - Information Fusion, 2023 - Elsevier
Estimating interest rates is a typical multivariate prediction problem that has garnered
considerable attention in the finance industry. However, the rising complexity of the …
considerable attention in the finance industry. However, the rising complexity of the …
Feature selection in machine learning: Methods and comparison
Nowadays, a huge amount of data is generated every day in continuous manner in every
hour and if the data is not utilized in the right or meaningful manner then this is just like …
hour and if the data is not utilized in the right or meaningful manner then this is just like …