Bayesian estimation for misclassification rate in linear discriminant analysis

2021 ◽  
Author(s):  
Koshiro Yonenaga ◽  
Akio Suzukawa
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Bayesian Estimation ◽  
Misclassification Rate ◽  
Linear Discriminant

Classification of Diesel Engine Health Using Sparse Linear Discriminant Analysis (SLDA)

2009 ◽  
Author(s):  
Neha Chandrachud ◽  
Ravindra Kakade ◽  
Peter H. Meckl ◽  
Galen B. King ◽  
Kristofer Jennings
Keyword(s):  
Steady State ◽  
Diesel Engine ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Optimal Number ◽  
Classification Model ◽  
Misclassification Rate ◽  
Linear Discriminant ◽  
State Data ◽  
Input Variables

With requirements for on-board diagnostics on diesel engines becoming more stringent for the coming model years, diesel engine manufacturers must improve their ability to identify fault conditions that lead to increased exhaust emissions. This paper proposes a statistical classifier model to identify the state of the engine, i.e. healthy or faulty, using an optimal number of sensors based on the data acquired from the engine. The classification model proposed in this paper is based on Sparse Linear Discriminant Analysis. This technique performs Linear Discriminant Analysis with a sparseness criterion imposed such that classification, dimension reduction and feature selection are merged into one step. It was concluded that the analysis technique could produce 0% misclassification rate for the steady-state data acquired from the diesel engine using five input variables. The classifier model was also extended to transient operation of the engine. The misclassification rate in the case of transient data was reduced from 31% to 26% by using the steady-state data trained classifier using thirteen variables.

scholarly journals Depth-Based Classification for Distributions with Nonconvex Support

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Daniel Hlubinka ◽  
Ondrej Vencalek
Keyword(s):  
Statistical Analysis ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Classification Problem ◽  
Data Depth ◽  
Misclassification Rate ◽  
Skewed Data ◽  
Weighted Version ◽  
Linear Discriminant ◽  
Normal Populations

Halfspace depth became a popular nonparametric tool for statistical analysis of multivariate data during the last two decades. One of applications of data depth considered recently in literature is the classification problem. The data depth approach is used instead of the linear discriminant analysis mostly to avoid the parametric assumptions and to get better classifier for data whose distribution is not elliptically symmetric, for example, skewed data. In our paper, we suggest to use weighted version of halfspace depth rather than the halfspace depth itself in order to obtain lower misclassification rate in the case of “nonconvex” distributions. Simulations show that the results of depth-based classifiers are comparable with linear discriminant analysis for two normal populations, while for nonelliptic distributions the classifier based on weighted halfspace depth outperforms both linear discriminant analysis and classifier based on the usual (nonweighted) halfspace depth.

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