An analytical study of modified multi-objective Harris Hawk Optimizer towards medical data feature selection
J Piri, P Mohapatra - Computers in Biology and Medicine, 2021 - Elsevier
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 …
The intersection of damage evaluation of fiber-reinforced composite materials with machine learning: A review
Machine learning (ML) has emerged as a useful predictive tool based on mathematical and
statistical relationships for various engineering problems. The pairing of structural health …
statistical relationships for various engineering problems. The pairing of structural health …
[HTML][HTML] An external attention-based feature ranker for large-scale feature selection
An important problem in data science, feature selection (FS) consists of finding the optimal
subset of features and eliminating irrelevant or redundant features. The FS task on high …
subset of features and eliminating irrelevant or redundant features. The FS task on high …
Bi-level ensemble method for unsupervised feature selection
Unsupervised feature selection is an important machine learning task and thus attracts
increasingly more attention. However, due to the absence of labels, unsupervised feature …
increasingly more attention. However, due to the absence of labels, unsupervised feature …
Deep feature screening: Feature selection for ultra high-dimensional data via deep neural networks
The applications of traditional statistical feature selection methods to high-dimension, low-
sample-size data often struggle and encounter challenging problems, such as overfitting …
sample-size data often struggle and encounter challenging problems, such as overfitting …
Graph convolutional network-based feature selection for high-dimensional and low-sample size data
Motivation Feature selection is a powerful dimension reduction technique which selects a
subset of relevant features for model construction. Numerous feature selection methods …
subset of relevant features for model construction. Numerous feature selection methods …
Evolutionary deep feature selection for compact representation of gigapixel images in digital pathology
Despite the recent progress in Deep Neural Networks (DNNs) to characterize histopathology
images, compactly representing a gigapixel whole-slide image (WSI) via salient features to …
images, compactly representing a gigapixel whole-slide image (WSI) via salient features to …
Estimating city-level poverty rate based on e-commerce data with machine learning
There are many big data sources in Indonesia, for example, data from social media, financial
transactions, transportation, call detail records, and e-commerce. These types of data have …
transactions, transportation, call detail records, and e-commerce. These types of data have …
Estimation of leaf nitrogen content in wheat based on fusion of spectral features and deep features from near infrared hyperspectral imagery
Nitrogen is an important indicator for monitoring wheat growth. The rapid development and
wide application of non-destructive detection provide many approaches for estimating leaf …
wide application of non-destructive detection provide many approaches for estimating leaf …
An improved binary dandelion algorithm using sine cosine operator and restart strategy for feature selection
J Dong, X Li, Y Zhao, J Ji, S Li, H Chen - Expert Systems with Applications, 2024 - Elsevier
Feature selection (FS) is an important data preprocessing technology for machine learning
and data mining. Metaheuristic algorithm (MH) has been widely used in feature selection …
and data mining. Metaheuristic algorithm (MH) has been widely used in feature selection …