Results 21 to 30 of about 4,336,619 (297)

Structure Preserving Non-negative Feature Self-Representation for Unsupervised Feature Selection

IEEE Access, 2017
Inspired by the importance of self-representation and structure-preserving ability of features, in this paper, we propose a novel unsupervised feature selection algorithm named structure-preserving non-negative feature self-representation (SPNFSR).
Wei Zhou, Chengdong Wu, Yugen Yi, Guoliang Luo   +3 more
doaj   +1 more source

Feature selection for modular GA-based classification [PDF]

, 2004
Genetic algorithms (GAs) have been used as conventional methods for classifiers to adaptively evolve solutions for classification problems. Feature selection plays an important role in finding relevant features in classification.
Guan, SU, Zhu, F, Zhu, F., Guan, S.
core   +1 more source

Feature extraction for epileptic seizure detection using machine learning

Current Medicine Research and Practice, 2020
Background: Epilepsy is a common neurological disorder and affects approximately 70 million people worldwide. The traditional approach used by neurologists for the detection of seizure is time consuming.
Renuka Mohan Khati, Rajesh Ingle
doaj   +1 more source

New Feature Selection Algorithm Based on Feature Stability and Correlation

IEEE Access, 2022
The analysis of a large amount of data with high dimensionality of rows and columns increases the load of machine learning algorithms. Such data are likely to have noise and consequently, obstruct the performance of machine learning algorithms.
Luai Al-Shalabi
doaj   +1 more source

Quadratic Mutual Information Feature Selection

Entropy, 2017
We propose a novel feature selection method based on quadratic mutual information which has its roots in Cauchy–Schwarz divergence and Renyi entropy. The method uses the direct estimation of quadratic mutual information from data samples using Gaussian ...
Davor Sluga, Uroš Lotrič
doaj   +1 more source

Metalearning for Feature Selection

CoRR, 2017
A general formulation of optimization problems in which various candidate solutions may use different feature-sets is presented, encompassing supervised classification, automated program learning and other cases. A novel characterization of the concept of a "good quality feature" for such an optimization problem is provided; and a proposal regarding ...
Ben Goertzel, Nil Geisweiller, Chris Poulin   +2 more
openaire   +2 more sources

Semi-supervised Partial Multi-label Feature Selection [PDF]

Jisuanji kexue
Multi-label feature selection is a technique for reducing feature dimensionality by filtering out a subset of features with distinguishing power from the original feature space.However,the traditional method faces the problem of labeling accuracy ...
WU You, WANG Jing, LI Peipei, HU Xuegang
doaj   +1 more source

CBFS: high performance feature selection algorithm based on feature clearness. [PDF]

PLoS ONE, 2012
BACKGROUND: The goal of feature selection is to select useful features and simultaneously exclude garbage features from a given dataset for classification purposes.
Minseok Seo, Sejong Oh
doaj   +1 more source

Threshold Adaptation for Improved Wrapper-Based Evolutionary Feature Selection

Biomimetics
Feature selection is essential for enhancing classification accuracy, reducing overfitting, and improving interpretability in high-dimensional datasets.
Uroš Mlakar, Iztok Fister, Iztok Fister
doaj   +1 more source

Supervised Feature Selection With a Stratified Feature Weighting Method

IEEE Access, 2018
Feature selection has been a powerful tool to handle high-dimensional data. Most of these methods are biased toward the highest rank features which may be highly correlated with each other.
Renjie Chen, Ning Sun, Xiaojun Chen, Min Yang, Qingyao Wu   +4 more
doaj   +1 more source