Classification Accuracy of Neural Networks vs. Discriminant Analysis, Logistic Regression, and Classification and Regression Trees

Methodology ◽  
2007 ◽  
Vol 3 (2) ◽  
pp. 47-57 ◽  
Author(s):  
Holmes Finch ◽  
Mercedes K. Schneider
Keyword(s):  
Neural Networks ◽  
Logistic Regression ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Regression Trees ◽  
Classification And Regression Trees ◽  
Quadratic Discriminant Analysis ◽  
Linear Discriminant ◽  
Classification And Regression ◽  
The Impact

Abstract. This paper compares the predictive accuracy of three commonly used parametric methods for group classification, linear discriminant analysis, quadratic discriminant analysis, and logistic regression, with two less common approaches, neural networks and classification and regression trees. The simulation study examined the impact of such factors as inequality of covariance matrices, distribution of predictors, and group size ratio (among others) on the performance of each method. Results indicate that quadratic discriminant analysis always performs as well as the other methods while neural networks behave very similarly to linear discriminant analysis and logistic regression.

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.

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.

scholarly journals Impact of Covid-19 on SME portfolios in Morocco: Evaluation of banking risk costs and the effectiveness of state support measures

2021 ◽  
Vol 18 (3) ◽  
pp. 260-276
Author(s):  
Mohamed Habachi ◽  
Salim El Haddad
Keyword(s):  
Logistic Regression ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Expected Loss ◽  
Linear Discriminant ◽  
Critical Situation ◽  
Support Measures ◽  
Risk Cost ◽  
Analysis Models ◽  
The Impact

This study proposed a method for constructing rating tools using logistic regression and linear discriminant analysis to determine the risk profile of SME portfolios. The objective, firstly, is to evaluate the impact of the crisis due to the Covid-19 by readjusting the profile of each company by using the expert opinion and, secondly, to evaluate the efficiency of the measures taken by the Moroccan state to support the companies during the period of the pandemic. The analysis in this paper showed that the performance of the logistic regression and linear discriminant analysis models is almost equivalent based on the ROC curve. However, it was revealed that the logistic regression model minimizes the risk cost represented in this study by the expected loss. For the support measures adopted by the Moroccan government, the study showed that the failure rate (critical situation) of the firms benefiting from the support is largely lower than that of the non-beneficiaries.

scholarly journals Decoding Attempted Hand Movements in Stroke Patients Using Surface Electromyography

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6763
Author(s):  
Mads Jochumsen ◽  
Imran Khan Niazi ◽  
Muhammad Zia ur Rehman ◽  
Imran Amjad ◽  
Muhammad Shafique ◽  
...  
Keyword(s):  
Neural Networks ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Convolutional Neural Networks ◽  
Classification Accuracy ◽  
Intraclass Correlation ◽  
Motor Impairment ◽  
Wrist Flexion ◽  
Stroke Patients ◽  
Linear Discriminant

Brain- and muscle-triggered exoskeletons have been proposed as a means for motor training after a stroke. With the possibility of performing different movement types with an exoskeleton, it is possible to introduce task variability in training. It is difficult to decode different movement types simultaneously from brain activity, but it may be possible from residual muscle activity that many patients have or quickly regain. This study investigates whether nine different motion classes of the hand and forearm could be decoded from forearm EMG in 15 stroke patients. This study also evaluates the test-retest reliability of a classical, but simple, classifier (linear discriminant analysis) and advanced, but more computationally intensive, classifiers (autoencoders and convolutional neural networks). Moreover, the association between the level of motor impairment and classification accuracy was tested. Three channels of surface EMG were recorded during the following motion classes: Hand Close, Hand Open, Wrist Extension, Wrist Flexion, Supination, Pronation, Lateral Grasp, Pinch Grasp, and Rest. Six repetitions of each motion class were performed on two different days. Hudgins time-domain features were extracted and classified using linear discriminant analysis and autoencoders, and raw EMG was classified with convolutional neural networks. On average, 79 ± 12% and 80 ± 12% (autoencoders) of the movements were correctly classified for days 1 and 2, respectively, with an intraclass correlation coefficient of 0.88. No association was found between the level of motor impairment and classification accuracy (Spearman correlation: 0.24). It was shown that nine motion classes could be decoded from residual EMG, with autoencoders being the best classification approach, and that the results were reliable across days; this may have implications for the development of EMG-controlled exoskeletons for training in the patient’s home.

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