A review of feature selection methods in medical applications
Feature selection is a preprocessing technique that identifies the key features of a given
problem. It has traditionally been applied in a wide range of problems that include biological …
problem. It has traditionally been applied in a wide range of problems that include biological …
Feature selection for high-dimensional data
This paper offers a comprehensive approach to feature selection in the scope of
classification problems, explaining the foundations, real application problems and the …
classification problems, explaining the foundations, real application problems and the …
Comparative analysis of different characteristics of automatic sleep stages
D Zhao, Y Wang, Q Wang, X Wang - Computer methods and programs in …, 2019 - Elsevier
Background and objective With the acceleration of social rhythm and the increase of
pressure, there are various sleep problems among people. Sleep staging is an important …
pressure, there are various sleep problems among people. Sleep staging is an important …
Risk prediction in life insurance industry using supervised learning algorithms
N Boodhun, M Jayabalan - Complex & Intelligent Systems, 2018 - Springer
Risk assessment is a crucial element in the life insurance business to classify the applicants.
Companies perform underwriting process to make decisions on applications and to price …
Companies perform underwriting process to make decisions on applications and to price …
Feature selection by integrating two groups of feature evaluation criteria
Feature selection is a preprocessing step in many application areas that are relevant to
expert and intelligent systems, such as data mining and machine learning. Feature selection …
expert and intelligent systems, such as data mining and machine learning. Feature selection …
Feature selection by optimizing a lower bound of conditional mutual information
A unified framework is proposed to select features by optimizing computationally feasible
approximations of high-dimensional conditional mutual information (CMI) between features …
approximations of high-dimensional conditional mutual information (CMI) between features …
A fuzzy neural network approach for automatic K-complex detection in sleep EEG signal
R Ranjan, R Arya, SL Fernandes, E Sravya… - Pattern Recognition …, 2018 - Elsevier
The study of sleep stages and the associated signals have emerged as a very important
parameter to identify the neurological disorders and test of mental activities nowadays …
parameter to identify the neurological disorders and test of mental activities nowadays …
Don't just sign use brain too: A novel multimodal approach for user identification and verification
In this paper, we propose a novel multimodal user identification and verification scheme
combining two inter-linked biometric traits, ie, signature and brain signals …
combining two inter-linked biometric traits, ie, signature and brain signals …
Multi-objective optimization of feature selection using hybrid cat swarm optimization
With the pervasive generation of information from a wide range of sensors and devices,
there always exist a large number of input features in databases, thus complicating machine …
there always exist a large number of input features in databases, thus complicating machine …
Detection of k-complexes in EEG signals using a multi-domain feature extraction coupled with a least square support vector machine classifier
W Al-Salman, Y Li, P Wen - Neuroscience Research, 2021 - Elsevier
Sleep scoring is one of the primary tasks for the classification of sleep stages using
electroencephalogram (EEG) signals. It is one of the most important diagnostic methods in …
electroencephalogram (EEG) signals. It is one of the most important diagnostic methods in …