scholarly journals Parametric classification with soft labels using the evidential EM algorithm: linear discriminant analysis versus logistic regression

2017 ◽  
Vol 11 (4) ◽  
pp. 659-690 ◽  
Author(s):  
Benjamin Quost ◽  
Thierry Denœux ◽  
Shoumei Li
Keyword(s):  
Logistic Regression ◽  
Discriminant Analysis ◽  
Em Algorithm ◽  
Linear Discriminant Analysis ◽  
Linear Discriminant ◽  
Parametric Classification

Methods of Evaluation for Region’s Landslide Susceptibility. Short Overview

2017 ◽  
Vol 6 (3) ◽  
pp. 57-60
Author(s):  
Денис Кривогуз ◽  
Denis Krivoguz
Keyword(s):  
Neural Networks ◽  
Logistic Regression ◽  
Artificial Neural Networks ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Landslide Susceptibility ◽  
Susceptibility Assessment ◽  
Linear Discriminant ◽  
Landslide Susceptibility Assessment ◽  
Artificial Neural

Modern approaches to the region’s landslide susceptibility assessment are considered in this paper. Have been presented descriptions of the most used techniques for landslide susceptibility assessment: logistic regression, indicator validity, linear discriminant analysis and application of artificial neural networks. These techniques’ advantages and disadvantages are discussed in the paper. The most suitable techniques for various conditions of analysis have been marked. It has been concluded that the most acceptable techniques of analysis for a large number of input data related to the studied region are the method of logistic regression and indicator validity method. With these methods the most accurate results are achieved. When there is a lack of information, it is more expedient to use linear discriminant analysis and artificial neural networks that will minimize potential analysis inaccuracies.

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 A COMPARATIVE STUDY OF THE MULTIPLE LOGISTIC REGRESSION, LINEAR DISCRIMINANT ANALYSIS AND QUADRATIC DISCRIMINANT FOR ESTIMATING THE MISCLASSIFICATION ERROR RATE OF INFANT BIRTH OUTCOME

2020 ◽  
Vol 8 (9) ◽  
pp. 358-367
Author(s):  
O. Akangoziri ◽  
C. N. Okoli
Keyword(s):  
Logistic Regression ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Error Rate ◽  
Birth Outcome ◽  
Misclassification Error ◽  
Multiple Logistic Regression ◽  
Linear Discriminant ◽  
Misclassification Error Rate ◽  
Infant Birth

This study examined comparison of the Multiple logistic regression, Linear discriminant analysis and Quadratic discriminant in estimating the infant birth outcome and misclassification error rate of birth outcomes with factors of infant mortality in Anambra State, Nigeria. The birth outcomes of interest were the Neonatal death, Still birth and Alive. Secondary source of data were obtained from the records department of General Hospital Onitsha from 2007-2016. The data comprises of Status of infant birth, Mothers parity, Age of mother, Weight of baby, Mothers Education Status, Number of Bookings before gestation and Gestation Age. The data analysis is performed using R-software. The result of the findings from the multiple logistic regression showed that Mothers Education Status (MES) and Booking contributed significantly on the logistic model while factors of Parity, Sex, Age of Mother (AOM), Year, GA and Birth Weight (BW) were found to be insignificant on birth outcomes. Also observed that the misclassification error rate for birth outcome for the said approach is found to be 0.5992 (59.92%). More so, findings of the study equally showed that the prior probabilities of the groups for the linear and quadratic discriminant analysis were 0.228503, 0.40168 and 0.36981 for Alive, Neonatal death and Still birth respectively. Further findings revealed that the Mothers Education Status and Bookings Status have the greatest impact for first and second linear function respectively. In addition, the result of the misclassification error rate for birth outcome using the linear discriminant analysis is 0.5931 (59.31%). The misclassification error rate for birth outcome based on   quadratic discriminant analysis is 0.5956 (59.56%). Based on the findings of this study, linear discriminant approach is the best alternative in estimating misclassification error rate of infant birth outcome followed by quadratic discriminant analysis and the least is multiple logistic regression. The findings clearly confirmed that the linear discriminant analysis is the best with misclassification error rate of 59.31%.

Comparison of logistic regression and linear discriminant analysis

2004 ◽  
Vol 1 (1) ◽  
pp. 143-161
Author(s):  
Maja Pohar ◽  
Mateja Blas ◽  
Sandra Turk
Keyword(s):  
Logistic Regression ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Predictive Accuracy ◽  
Classification Models ◽  
Linear Classification ◽  
Robust Method ◽  
Linear Discriminant ◽  
Explanatory Variables ◽  
The Impact

Two of the most widely used statistical methods for analyzing categorical outcome variables are linear discriminant analysis and logistic regression. While both are appropriate for the development of linear classification models, linear discriminant analysis makes more assumptions about the underlying data. Hence, it is assumed that logistic regression is the more flexible and more robust method in case of violations of these assumptions. In this paper we consider the problem of choosing between the two methods, and set some guidelines for proper choice. The comparison between the methods is based on several measures of predictive accuracy. The performance of the methods is studied by simulations. We start with an example where all the assumptions of the linear discriminant analysis are satisfied and observe the impact of changes regarding the sample size, covariance matrix, Mahalanobis distance and direction of distance between group means. Next, we compare the robustness of the methods towards categorisation and non-normality of explanatory variables in a closely controlled way. We show that the results of LDA and LR are close whenever the normality assumptions are not too badly violated, and set some guidelines for recognizing these situations. We discuss the inappropriateness of LDA in all other cases.

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