Results 31 to 40 of about 53,359 (288)

Unsupervised Representative Feature Selection Algorithm Based on Information Entropy and Relevance Analysis

IEEE Access, 2018
Feature selection plays an important role in preprocessing in pattern recognition and data mining, especially in large scale image, digital text, and biological data.
Yintong Wang
doaj   +1 more source

SHAP-Based Feature Selection for Enhanced Unsupervised Labeling

IEEE Access
Manual dataset labeling is expensive, time-consuming, and susceptible to noise and inaccuracies, often necessitating significant financial investments with risks of inconsistencies from human annotations.
Mary Anne Walauskis, Taghi M. Khoshgoftaar   +1 more
doaj   +1 more source

Unsupervised Feature Selection With Ordinal Preserving Self-Representation

IEEE Access, 2018
Unsupervised feature selection is designed to select an optimal feature subset without any label information from high-dimensional data, which is implemented by eliminating the irrelevant and redundant features and has been attracted widespread attention
Jiangyan Dai, Yuqi Chen, Yugen Yi, Jining Bao, Lei Wang, Wei Zhou, Gang Lei   +6 more
doaj   +1 more source

Efficient Feature Ranking and Selection Using Statistical Moments

IEEE Access
Unsupervised feature selection methods can be more efficient than supervised methods, which rely on the expensive and time-consuming data labeling process.
Yael Hochma, Yuval Felendler, Mark Last
doaj   +1 more source

Feature selection for modular GA-based classification

, 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

Cluster Density Properties Define a Graph for Effective Pattern Feature Selection

IEEE Access, 2020
Feature selection is a challenging problem that occurs in the high-dimensional data analysis of many major applications. It addresses the curse of dimensionality by determining a small set of features to represent high-dimensional data without ...
Khadidja Henni, Neila Mezghani, Amar Mitiche   +2 more
doaj   +1 more source

Novel feature selection method via kernel tensor decomposition for improved multi-omics data analysis

BMC Medical Genomics, 2022
Background Feature selection of multi-omics data analysis remains challenging owing to the size of omics datasets, comprising approximately $$10^2$$ 10 2 – $$10^5$$ 10 5 features. In particular, appropriate methods to weight individual omics datasets are
Y-h. Taguchi, Turki Turki
doaj   +1 more source

Hypergraph Spectra for Unsupervised Feature Selection [PDF]

, 2012
Most existing feature selection methods focus on ranking individual features based on a utility criterion, and select the optimal feature set in a greedy manner. However, the feature combinations found in this way do not give optimal classification performance, since they neglect the correlations among features.
Zhihong Zhang 0001, Edwin R. Hancock
openaire   +1 more source

Modular feature selection using relative importance factors

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

Unsupervised Feature Selection Using Nonnegative Spectral Analysis

, 2021
In this paper, a new unsupervised learning algorithm, namely Nonnegative Discriminative Feature Selection (NDFS), is proposed. To exploit the discriminative information in unsupervised scenarios, we perform spectral clustering to learn the cluster labels
Lu, Hanqing, Li, Zechao, Liu, Jing, Zhou, Xiaofang, Yang, Yi   +4 more
core   +1 more source