Diagnosing Influential Observations in Quadratic Discriminant Analysis

Biometrics ◽  
1996 ◽  
Vol 52 (4) ◽  
pp. 1235 ◽  
Author(s):  
Wing K. Fung
Keyword(s):  
Discriminant Analysis ◽  
Quadratic Discriminant Analysis ◽  
Influential Observations

Quality based classification of gasoline samples by ATR-FTIR spectrometry using spectral feature selection with quadratic discriminant analysis

Fuel ◽  
2013 ◽  
Vol 111 ◽  
pp. 96-102 ◽  
Author(s):  
Mohammadreza Khanmohammadi ◽  
Amir Bagheri Garmarudi ◽  
Keyvan Ghasemi ◽  
Miguel de la Guardia
Keyword(s):  
Feature Selection ◽  
Discriminant Analysis ◽  
Spectral Feature ◽  
Quadratic Discriminant Analysis ◽  
Ftir Spectrometry ◽  
Spectral Feature Selection

Using Quadratic Discriminant Analysis by Intrusion Detection Systems for Port Scan and Slowloris Attack Classification

2021 ◽  
pp. 188-200
Author(s):  
Vinícius M. Deolindo ◽  
Bruno L. Dalmazo ◽  
Marcus V. B. da Silva ◽  
Luiz R. B. de Oliveira ◽  
Allan de B. Silva ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Intrusion Detection ◽  
Intrusion Detection Systems ◽  
Quadratic Discriminant Analysis ◽  
Detection Systems ◽  
Port Scan

Graphical Tools for Quadratic Discriminant Analysis

Technometrics ◽  
2007 ◽  
Vol 49 (2) ◽  
pp. 172-183 ◽  
Author(s):  
Iain Pardoe ◽  
Xiangrong Yin ◽  
R. Dennis Cook
Keyword(s):  
Discriminant Analysis ◽  
Quadratic Discriminant Analysis

scholarly journals Automated Classification of Regional Meteorological Events in a Coastal Area Using In Situ Measurements

2020 ◽  
Vol 37 (4) ◽  
pp. 723-739 ◽  
Author(s):  
Anton Sokolov ◽  
Egor Dmitriev ◽  
Cyril Gengembre ◽  
Hervé Delbarre
Keyword(s):  
Discriminant Analysis ◽  
Coastal Area ◽  
Supervised Classification ◽  
Nearest Neighbors ◽  
Support Vector ◽  
Classification Algorithms ◽  
Quadratic Discriminant Analysis ◽  
Automated Classification ◽  
Sea Breezes

AbstractThe problem is considered of atmospheric meteorological events’ classification, such as sea breezes, fogs, and high winds, in coastal areas. In situ wind, temperature, humidity, pressure, radiance, and turbulence meteorological measurements are used as predictors. Local atmospheric events of 2013–14 were analyzed and classified manually using data of the measurement campaign in the coastal area of the English Channel in Dunkirk, France. The results of that categorization allowed the training of a few supervised classification algorithms using the data of an ultrasonic anemometer as predictors. The comparison was carried out for the K-nearest-neighbors classifier, support vector machine, and two Bayesian classifiers—quadratic discriminant analysis and Parzen–Rozenblatt window. The analysis showed that the K-nearest-neighbors and quadratic discriminant analysis classifiers reveal the best classification accuracy (up to 80% correctly classified meteorological events). The latter classifier has higher calculation speed and is less sensitive to unbalanced data and the overtraining problem. The most informative atmospheric parameters for events recognition were revealed for each algorithm. The results obtained showed that supervised classification algorithms contribute to automation of processing and analyzing of local meteorological measurements.

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