Feature Selection for Identifying Critical Variables of Principal Components Based on K-Nearest Neighbor Rule

2007 ◽  
pp. 193-204
Author(s):  
Yun Li ◽  
Bao-Liang Lu
Keyword(s):  
Feature Selection ◽  
Principal Components ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Rule ◽  
Selection For

COMBINING FEATURE SELECTION WITH EXTRACTION: UNSUPERVISED FEATURE SELECTION BASED ON PRINCIPAL COMPONENT ANALYSIS

2009 ◽  
Vol 18 (06) ◽  
pp. 883-904
Author(s):  
YUN LI ◽  
BAO-LIANG LU ◽  
TENG-FEI ZHANG
Keyword(s):  
Feature Selection ◽  
Principal Components ◽  
Nearest Neighbor ◽  
Dimensional Space ◽  
Image Data ◽  
Principal Component ◽  
Data Sets ◽  
K Nearest Neighbor ◽  
Unsupervised Feature Selection ◽  
Linear Feature

Principal components analysis (PCA) is a popular linear feature extractor, and widely used in signal processing, face recognition, etc. However, axes of the lower-dimensional space, i.e., principal components, are a set of new variables carrying no clear physical meanings. Thus we propose unsupervised feature selection algorithms based on eigenvectors analysis to identify critical original features for principal component. The presented algorithms are based on k-nearest neighbor rule to find the predominant row components and eight new measures are proposed to compute the correlation between row components in transformation matrix. Experiments are conducted on benchmark data sets and facial image data sets for gender classification to show their superiorities.

Accelerating wrapper-based feature selection with K-nearest-neighbor

2015 ◽  
Vol 83 ◽  
pp. 81-91 ◽  
Author(s):  
Aiguo Wang ◽  
Ning An ◽  
Guilin Chen ◽  
Lian Li ◽  
Gil Alterovitz
Keyword(s):  
Feature Selection ◽  
Nearest Neighbor ◽  
K Nearest Neighbor

Feature Selection Method for Hydraulic System Faults Diagnosis Based on GA-PLS

2010 ◽  
Vol 44-47 ◽  
pp. 1130-1134
Author(s):  
Sheng Li ◽  
Pei Lin Zhang ◽  
Bing Li
Keyword(s):  
Feature Selection ◽  
Hydraulic System ◽  
Nearest Neighbor ◽  
Feature Selection Method ◽  
Original Data ◽  
Selection Method ◽  
Classification Model ◽  
K Nearest Neighbor ◽  
K Nearest Neighbor Algorithm ◽  
Faults Diagnosis

Feature selection is a key step in hydraulic system fault diagnosis. Some of the collected features are unrelated to classification model, and some are high correlated to other features. These features are harmful for establishing classification model. In order to solve this problem, genetic algorithm-partial least squares (GA-PLS) is proposed for selecting the representative and optimal features. K nearest neighbor algorithm (KNN) is used for diagnosing and classifying hydraulic system faults. For expressing better performance of GA-PLS, the original data of a model engineering hydraulic system is used, and the results of GA-PLS are compared with all feature used and GA. The experimental results show that, the proposed feature method can diagnose and classify hydraulic system faults more efficiently with using fewer features.

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