A comprehensive survey on feature selection in the various fields of machine learning
Abstract In Machine Learning (ML), Feature Selection (FS) plays a crucial part in reducing
data's dimensionality and enhancing any proposed framework's performance. However, in …
data's dimensionality and enhancing any proposed framework's performance. However, in …
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 …
Survey on evolutionary deep learning: Principles, algorithms, applications, and open issues
Over recent years, there has been a rapid development of deep learning (DL) in both
industry and academia fields. However, finding the optimal hyperparameters of a DL model …
industry and academia fields. However, finding the optimal hyperparameters of a DL model …
An analytical study of modified multi-objective Harris Hawk Optimizer towards medical data feature selection
Abstract Dimensionality reduction or Feature Selection (FS) is a multi-target optimization
problem with two goals: improving the classification efficiency while simultaneously …
problem with two goals: improving the classification efficiency while simultaneously …
Explainable AI and machine learning: performance evaluation and explainability of classifiers on educational data mining inspired career counseling
Abstract Machine Learning concept learns from experiences, inferences and conceives
complex queries. Machine learning techniques can be used to develop the educational …
complex queries. Machine learning techniques can be used to develop the educational …
A review on dimensionality reduction techniques
X Huang, L Wu, Y Ye - … Journal of Pattern Recognition and Artificial …, 2019 - World Scientific
High-dimensional data is ubiquitous in scientific research and industrial production fields. It
brings a lot of information to people, at the same time, because of its sparse and …
brings a lot of information to people, at the same time, because of its sparse and …
Propension to customer churn in a financial institution: A machine learning approach
This paper examines churn prediction of customers in the banking sector using a unique
customer-level dataset from a large Brazilian bank. Our main contribution is in exploring this …
customer-level dataset from a large Brazilian bank. Our main contribution is in exploring this …
[HTML][HTML] A survey of neural network-based cancer prediction models from microarray data
M Daoud, M Mayo - Artificial intelligence in medicine, 2019 - Elsevier
Neural networks are powerful tools used widely for building cancer prediction models from
microarray data. We review the most recently proposed models to highlight the roles of …
microarray data. We review the most recently proposed models to highlight the roles of …
Dimensionality reduction approach based on modified hunger games search: case study on Parkinson's disease phonation
Abstract Hunger Games Search (HGS) is a newly developed swarm-based algorithm
inspired by the cooperative behavior of animals and their hunting strategies to find prey …
inspired by the cooperative behavior of animals and their hunting strategies to find prey …
Clinical data classification using an enhanced SMOTE and chaotic evolutionary feature selection
Class imbalance and the presence of irrelevant or redundant features in training data can
pose serious challenges to the development of a classification framework. This paper …
pose serious challenges to the development of a classification framework. This paper …