Use of VNS and TS in classification: variable selection and determination of the linear discrimination function coefficients

2007 ◽  
Vol 18 (2) ◽  
pp. 191-206 ◽  
Author(s):  
Joaquín Pacheco ◽  
Silvia Casado ◽  
Laura Nuñez
Keyword(s):  
Variable Selection ◽  
Discrimination Function ◽  
Linear Discrimination ◽  
Classification Variable

Determination of Meso-Compositions in Granite Images Using Bayes Discriminant Analysis

2011 ◽  
Vol 243-249 ◽  
pp. 2276-2282
Author(s):  
Jin Ming Xu ◽  
Yi Jun Zhou ◽  
Chen Da Lu
Keyword(s):  
Image Processing ◽  
Correlation Energy ◽  
Bayes Rule ◽  
Discrimination Function ◽  
Original Image ◽  
Bayes Classifier ◽  
Linear Discrimination ◽  
Average Variance ◽  
Bayes Discriminant Analysis

Granite is generally composed of the minerals such as quartz, feldspar and mica. Distinguishing types and distributions of these meso-compositions are of significantly importance in analyzing the properties of granite in practice. In the current study, the Bayes rule was used to classify the compositions in granite images. The grayscale threshold based technique was used to initially distinguish the meso-compositions in the original image. Six parameters, including the average, variance, contrast, correlation, energy, homogeneity of grayscale values, were extracted from the granite images and selected as the variables characterized minerals in the classifier. A linear discrimination function is then obtained. The meso-compositions of the granite images from different sites were used for the purpose of validation. The image processing was coded into a program and can be automatically run. The result shows that the grayscale threshold based technique and Bayes classifier proposed herein may provide a lot of valuable information both in distinguishing the meso- compositions in rock materials and in analyzing those in the field of the civil engineering.

Determination of API Gravity and Total and Basic Nitrogen Content by Mid- and Near-Infrared Spectroscopy in Crude Oil with Multivariate Regression and Variable Selection Tools

2019 ◽  
Vol 52 (18) ◽  
pp. 2914-2930 ◽  
Author(s):  
Karla Pereira Rainha ◽  
Júlia Tristão do Carmo Rocha ◽  
Rayza Rosa Tavares Rodrigues ◽  
Betina Pires de Oliveira Lovatti ◽  
Eustáquio Vinicius Ribeiro de Castro ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Variable Selection ◽  
Crude Oil ◽  
Nitrogen Content ◽  
Near Infrared Spectroscopy ◽  
Multivariate Regression ◽  
Near Infrared ◽  
Basic Nitrogen ◽  
Selection Tools

Determination of Iodine Value of Palm Oils Using Partial Least Squares Regression-Fourier Transform Infrared Data

2014 ◽  
Vol 70 (5) ◽  
Author(s):  
Nor Fazila Rasaruddin ◽  
Mas Ezatul Nadia Mohd Ruah ◽  
Mohamed Noor Hasan ◽  
Mohd Zuli Jaafar
Keyword(s):  
Fourier Transform ◽  
Variable Selection ◽  
Least Squares ◽  
Partial Least Squares ◽  
Correlation Coefficient ◽  
Iodine Value ◽  
Cross Validation ◽  
Pls Regression ◽  
Pure Sample

This paper shows the determination of iodine value (IV) of pure and frying palm oils using Partial Least Squares (PLS) regression with application of variable selection. A total of 28 samples consisting of pure and frying palm oils which acquired from markets. Seven of them were considered as high-priced palm oils while the remaining was low-priced. PLS regression models were developed for the determination of IV using Fourier Transform Infrared (FTIR) spectra data in absorbance mode in the range from 650 cm-1 to 4000 cm-1. Savitzky Golay derivative was applied before developing the prediction models. The models were constructed using wavelength selected in the FTIR region by adopting selectivity ratio (SR) plot and correlation coefficient to the IV parameter. Each model was validated through Root Mean Square Error Cross Validation, RMSECV and cross validation correlation coefficient, R2cv. The best model using SR plot was the model with mean centring for pure sample and model with a combination of row scaling and standardization of frying sample. The best model with the application of the correlation coefficient variable selection was the model with a combination of row scaling and standardization of pure sample and model with mean centering data pre-processing for frying sample. It is not necessary to row scaled the variables to develop the model since the effect of row scaling on model quality is insignificant.

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