Visual comparison based on linear regression model and linear discriminant analysis

2018 ◽  
Vol 57 ◽  
pp. 118-124 ◽  
Author(s):  
Hanqin Shi ◽  
Liang Tao
Keyword(s):  
Discriminant Analysis ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Discriminant Analysis ◽  
Linear Regression Model ◽  
Linear Discriminant ◽  
Visual Comparison

scholarly journals PLS Generalized Linear Regression and Kernel Multilogit Algorithm (KMA) for Microarray Data Classification Problem

2020 ◽  
Vol 43 (2) ◽  
pp. 233-249
Author(s):  
Adolphus Wagala ◽  
Graciela González-Farías ◽  
Rogelio Ramos ◽  
Oscar Dalmau
Keyword(s):  
Logistic Regression ◽  
Discriminant Analysis ◽  
Linear Regression ◽  
Linear Discriminant Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Classification Error ◽  
Support Vector ◽  
Linear Discriminant ◽  
Generalized Linear Regression

This study involves the implentation of the extensions of the partial least squares generalized linear regression (PLSGLR) by combining  it with logistic regression and  linear  discriminant analysis,  to  get a  partial least  squares generalized linear  regression-logistic regression model (PLSGLR-log),  and a partial least squares generalized linear regression-linear discriminant analysis model (PLSGLRDA). A comparative  study  of  the obtained  classifiers with   the   classical  methodologies like  the k-nearest  neighbours (KNN), linear   discriminant  analysis  (LDA),   partial  least  squares discriminant analysis (PLSDA),  ridge  partial least squares (RPLS), and  support vector machines(SVM)  is  then  carried  out.    Furthermore,  a  new  methodology known as kernel multilogit algorithm (KMA) is also implemented and its performance compared with those of the other classifiers. The KMA emerged as the best classifier based  on the lowest  classification error  rates  compared to  the  others  when  applied   to  the  types   of data   are considered;  the  un- preprocessed and preprocessed.

scholarly journals Reconocimiento de patrones aplicando LDA y LR a señales optoelectrónicas de sistemas de barrido óptico

2020 ◽  
Vol 17 (4) ◽  
pp. 401
Author(s):  
J. E. Miranda-Vega ◽  
M. Rivas-López ◽  
W. Flores-Fuentes ◽  
O. Sergiyenko ◽  
L. Lindner ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Linear Regression ◽  
Linear Discriminant Analysis ◽  
Predictive Coding ◽  
Linear Predictive Coding ◽  
Linear Discriminant ◽  
Optical Scanning ◽  
Scanning Systems

<p class="icsmabstract">Este artículo da seguimiento a previas experimentaciones actualmente publicadas acerca de la minimización de ruido ópticoeléctrico en los sistemas de barrido óptico OSS (en inglés, Optical Scanning Systems), implementando técnicas computacionales para el reconocimiento de los patrones que se generan de cada fuente de referencia y que son utilizadas para indicar una coordenada que el OSS monitoreará. Técnicas como análisis linear discriminante LDA (en inglés, Linear Discriminant Analysis) y regresión lineal LR (en inglés, Linear Regression) fueron implementadas para discriminar las señales causadas por otras fuentes distintas a las de referencia. Para aumentar la eficiencia de estos modelos fueron implementados codificación predictiva lineal LPC (en inglés, Linear Predictive Coding) y Cuantiles como extractores de características. Los resultados fueron alentadores con tasas de reconocimiento mayores al 91.2 %, alcanzando en algunos casos una exactitud del 100 %.</p>

Physico-Chemical Analysis of Groundwater and Effect of Climatic Change in Angul-Talcher Area

2018 ◽  
Vol 6 (12) ◽  
pp. 26
Author(s):  
Aliva Bera ◽  
D.P. Satapathy
Keyword(s):  
Water Resources ◽  
Linear Regression ◽  
Regression Model ◽  
Climatic Change ◽  
Linear Regression Model ◽  
Ann Model ◽  
Quality Of Water ◽  
Physico Chemical ◽  
Simulation Based

In this paper, the linear regression model using ANN and the linear regression model using MS Excel were developed to estimate the physico-chemical concentrations in groundwater using pH, EC, TDS, TH, HCO3 as input parameters and Ca, Mg and K as output parameters. A comparison was made which indicated that ANN model had the better ability to estimate the physic-chemical concentrations in groundwater. An analytical survey along with simulation based tests for finding the climatic change and its effect on agriculture and water bodies in Angul-Talcher area is done. The various seasonal parameters such as pH, BOD, COD, TDS,TSS along with heavy elements like Pb, Cd, Zn, Cu, Fe, Mn concentration in water resources has been analyzed. For past 30 years rainfall data has been analyzed and water quality index values has been studied to find normal and abnormal quality of water resources and matlab based simulation has been done for performance analysis. All results has been analyzed and it is found that the condition is stable. 

Prediction of Surface Roughness and Optimization of Cutting Parameters in CNC Turning of Rotational Features

2020 ◽  
Vol 38 (8A) ◽  
pp. 1143-1153
Author(s):  
Yousif K. Shounia ◽  
Tahseen F. Abbas ◽  
Raed R. Shwaish
Keyword(s):  
Surface Roughness ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Feed Rate ◽  
Cutting Parameters ◽  
Spindle Speed ◽  
Depth Of Cut ◽  
Non Linear ◽  
Optimization Of Cutting Parameters

This research presents a model for prediction surface roughness in terms of process parameters in turning aluminum alloy 1200. The geometry to be machined has four rotational features: straight, taper, convex and concave, while a design of experiments was created through the Taguchi L25 orthogonal array experiments in minitab17 three factors with five Levels depth of cut (0.04, 0.06, 0.08, 0.10 and 0.12) mm, spindle speed (1200, 1400, 1600, 1800 and 2000) r.p.m and feed rate (60, 70, 80, 90 and 100) mm/min. A multiple non-linear regression model has been used which is a set of statistical extrapolation processes to estimate the relationships input variables and output which the surface roughness which prediction outside the range of the data. According to the non-linear regression model, the optimum surface roughness can be obtained at 1800 rpm of spindle speed, feed-rate of 80 mm/min and depth of cut 0.04 mm then the best surface roughness comes out to be 0.04 μm at tapper feature at depth of cut 0.01 mm and same spindle speed and feed rate pervious which gives the error of 3.23% at evolution equation.

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