Movement recognition technology as a method of assessing spontaneous general movements in high risk infants
Preterm birth is associated with increased risks of neurological and motor impairments such
as cerebral palsy. The risks are highest in those born at the lowest gestations. Early …
as cerebral palsy. The risks are highest in those born at the lowest gestations. Early …
A survey on evolutionary computation approaches to feature selection
Feature selection is an important task in data mining and machine learning to reduce the
dimensionality of the data and increase the performance of an algorithm, such as a …
dimensionality of the data and increase the performance of an algorithm, such as a …
An improved binary sparrow search algorithm for feature selection in data classification
Feature Selection (FS) is an important preprocessing step that is involved in machine
learning and data mining tasks for preparing data (especially high-dimensional data) by …
learning and data mining tasks for preparing data (especially high-dimensional data) by …
Evolutionary computation for feature selection in classification problems
B De La Iglesia - Wiley Interdisciplinary Reviews: Data Mining …, 2013 - Wiley Online Library
Feature subset selection (FSS) has received a great deal of attention in statistics, machine
learning, and data mining. Real world data analyzed by data mining algorithms can involve …
learning, and data mining. Real world data analyzed by data mining algorithms can involve …
Review on wrapper feature selection approaches
N El Aboudi, L Benhlima - 2016 international conference on …, 2016 - ieeexplore.ieee.org
The main objective of feature selection process consists of investigating the optimal feature
subset leading to better classification quality while spending less computational cost …
subset leading to better classification quality while spending less computational cost …
A hybrid genetic algorithm with wrapper-embedded approaches for feature selection
Feature selection is an important research area for big data analysis. In recent years, various
feature selection approaches have been developed, which can be divided into four …
feature selection approaches have been developed, which can be divided into four …
Different metaheuristic strategies to solve the feature selection problem
SC Yusta - Pattern Recognition Letters, 2009 - Elsevier
This paper investigates feature subset selection for dimensionality reduction in machine
learning. We provide a brief overview of the feature subset selection techniques that are …
learning. We provide a brief overview of the feature subset selection techniques that are …
Benign and malignant breast tumor classification in ultrasound and mammography images via fusion of deep learning and handcraft features
C Cruz-Ramos, O García-Avila, JA Almaraz-Damian… - Entropy, 2023 - mdpi.com
Breast cancer is a disease that affects women in different countries around the world. The
real cause of breast cancer is particularly challenging to determine, and early detection of …
real cause of breast cancer is particularly challenging to determine, and early detection of …
A hybrid supervised machine learning classifier system for breast cancer prognosis using feature selection and data imbalance handling approaches
Nowadays, breast cancer is the most frequent cancer among women. Early detection is a
critical issue that can be effectively achieved by machine learning (ML) techniques. Thus in …
critical issue that can be effectively achieved by machine learning (ML) techniques. Thus in …
Automatic digital modulation recognition using artificial neural network and genetic algorithm
Automatic recognition of digital modulation signals has seen increasing demand nowadays.
The use of artificial neural networks for this purpose has been popular since the late 1990s …
The use of artificial neural networks for this purpose has been popular since the late 1990s …