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vfonsecad/unsupervised-sample-selection: publication

, 2021
This repository is maintained at https://github.com/vfonsecad/unsupervised-sample ...
Bart De Ketelaere, Wouter Saeys, Ben Aernouts, Valeria Fonseca Diaz   +3 more
core   +1 more source

Modular feature selection using relative importance factors [PDF]

, 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 Local Feature Hashing for Image Similarity Search [PDF]

, 2016
The potential value of hashing techniques has led to it becoming one of the most active research areas in computer vision and multimedia. However, most existing hashing methods for image search and retrieval are based on global feature representations ...
Liu, Li, Shao, Ling, Yu, Mengyang
core   +1 more source

Unsupervised Cluster-Wise Hyperspectral Band Selection for Classification

Remote Sensing, 2022
A hyperspectral image provides fine details about the scene under analysis, due to its multiple bands. However, the resulting high dimensionality in the feature space may render a classification task unreliable, mainly due to overfitting and the Hughes ...
Mateus Habermann, Elcio Hideiti Shiguemori, Vincent Frémont   +2 more
doaj   +1 more source

Structure Preserving Unsupervised Feature Selection Based on Autoencoder and Manifold Regularization [PDF]

Jisuanji kexue, 2021
There are a lot of redundant and irrelevant features in high-dimensional data,which seriously affect the efficiency and quality of data mining and the generalization performance of machine learning.Therefore,feature selection has become an important ...
YANG Lei, JIANG Ai-lian, QIANG Yan
doaj   +1 more source

A Fast and Intelligent Open-Circuit Fault Diagnosis Method for a Five-Level NNPP Converter Based on an Improved Feature Extraction and Selection Model

IEEE Access, 2020
The open-circuit faults of power semiconductor devices in multilevel converters are generally diagnosed by analyzing circuit signals. For converters with five or more levels, the difficulty of fault detection increases with increasing topological ...
Shu Ye, Jianguo Jiang, Zhongzheng Zhou, Cong Liu, Yunlong Liu   +4 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

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 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

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