Comparing the Feature Selection Using the Distributed Non-overlap Area Measurement Method with Principal Component Analysis

2009 ◽  
Author(s):  
Sang-Hong Lee ◽  
Dong-Kun Shin ◽  
Zhen-Xing Zhang ◽  
Joon S. Lim
Keyword(s):  
Principal Component Analysis ◽  
Feature Selection ◽  
Measurement Method ◽  
Principal Component ◽  
Component Analysis ◽  
Area Measurement ◽  
Overlap Area

scholarly journals Neural network design and feature selection using principal component analysis and Taguchi method for identifying wood veneer defects

2014 ◽  
Vol 2 (1) ◽  
pp. 291-308 ◽  
Author(s):  
Baris Yuce ◽  
Ernesto Mastrocinque ◽  
Michael Sylvester Packianather ◽  
Duc Pham ◽  
Alfredo Lambiase ◽  
...  
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Feature Selection ◽  
Taguchi Method ◽  
Network Design ◽  
Principal Component ◽  
Component Analysis ◽  
Wood Veneer

scholarly journals Multivariate Analysis and Machine Learning for Ripeness Classification of Cape Gooseberry Fruits

Processes ◽  
2019 ◽  
Vol 7 (12) ◽  
pp. 928 ◽  
Author(s):  
Miguel De-la-Torre ◽  
Omar Zatarain ◽  
Himer Avila-George ◽  
Mirna Muñoz ◽  
Jimy Oblitas ◽  
...  
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Feature Selection ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Color Spaces ◽  
Combination Methods ◽  
Fruit Samples ◽  
Cape Gooseberry

This paper explores five multivariate techniques for information fusion on sorting the visual ripeness of Cape gooseberry fruits (principal component analysis, linear discriminant analysis, independent component analysis, eigenvector centrality feature selection, and multi-cluster feature selection.) These techniques are applied to the concatenated channels corresponding to red, green, and blue (RGB), hue, saturation, value (HSV), and lightness, red/green value, and blue/yellow value (L*a*b) color spaces (9 features in total). Machine learning techniques have been reported for sorting the Cape gooseberry fruits’ ripeness. Classifiers such as neural networks, support vector machines, and nearest neighbors discriminate on fruit samples using different color spaces. Despite the color spaces being equivalent up to a transformation, a few classifiers enable better performances due to differences in the pixel distribution of samples. Experimental results show that selection and combination of color channels allow classifiers to reach similar levels of accuracy; however, combination methods still require higher computational complexity. The highest level of accuracy was obtained using the seven-dimensional principal component analysis feature space.

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