scholarly journals ESTIMATING THE PROBABILITY OF MISCLASSIFICATIONS IN TWO-GROUPS DISCRIMINANT ANALYSIS

2004 ◽  
Vol 3 (1) ◽  
pp. 1
Author(s):  
I W. MANGKU
Keyword(s):  
Discriminant Analysis ◽  
Discriminant Function ◽  
Comparative Studies ◽  
Linear Discriminant Function ◽  
Classification Rule ◽  
Survey Study ◽  
Linear Discriminant ◽  
Empirical Estimators

This paper is a survey study on estimation of the pro- bability of misclassifications in two-groups discriminant analysis using the linear discriminant function as the classification rule. Here we consider two groups of estimators, namely parametric esti- mators and empirical estimators. The results of some comparative studies on the performances of the considered estimators are also discussed.

scholarly journals Classification of all-rounders in limited over cricket - a machine learning approach

2021 ◽  
Vol 6 (4) ◽  
pp. 295-306
Author(s):  
Ananda B. W. Manage ◽  
Ram C. Kafle ◽  
Danush K. Wijekularathna
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Logistic Regression ◽  
Discriminant Function ◽  
Linear Discriminant Function ◽  
Classification Rule ◽  
Support Vector ◽  
Classification Methods ◽  
Linear Discriminant ◽  
Quadratic Discriminant Function

In cricket, all-rounders play an important role. A good all-rounder should be able to contribute to the team by both bat and ball as needed. However, these players still have their dominant role by which we categorize them as batting all-rounders or bowling all-rounders. Current practice is to do so by mostly subjective methods. In this study, the authors have explored different machine learning techniques to classify all-rounders into bowling all-rounders or batting all-rounders based on their observed performance statistics. In particular, logistic regression, linear discriminant function, quadratic discriminant function, naïve Bayes, support vector machine, and random forest classification methods were explored. Evaluation of the performance of the classification methods was done using the metrics accuracy and area under the ROC curve. While all the six methods performed well, logistic regression, linear discriminant function, quadratic discriminant function, and support vector machine showed outstanding performance suggesting that these methods can be used to develop an automated classification rule to classify all-rounders in cricket. Given the rising popularity of cricket, and the increasing revenue generated by the sport, the use of such a prediction tool could be of tremendous benefit to decision-makers in cricket.

A Note on Huberty and Wisenbaker’s “Views of Variable Importance”

1997 ◽  
Vol 22 (3) ◽  
pp. 309-322 ◽  
Author(s):  
D. Roland Thomas
Keyword(s):  
Discriminant Analysis ◽  
Discriminant Function ◽  
Operational Definition ◽  
Variable Importance ◽  
Linear Discriminant Function ◽  
Linear Discriminant ◽  
Additional Information ◽  
Definition Of

This article investigates criteria for assessing variable importance in MANOVA and descriptive discriminant analysis. Two criteria suggested by Huberty and Wisenbaker (1992) are examined, namely, (a) contribution to linear discriminant function scores and (b) contribution to grouping variable effects. Thomas and Zumbo (1996) have shown that the first criterion can be operationalized using discriminant ratio coefficients (DRCs). It is shown in this article that DRCs also provide an operational definition of grouping variable effects. Thus, it is proposed that the two criteria be amalgamated and called the contribution to grouping effects and discriminant scores. The F-to-remove indexes used by Huberty and Wisenbaker can then be regarded as operational definitions of a separate criterion, namely, the amount of additional information contributed to group discrimination.

The Research on the EC Individual Credibility Based on Linear Discriminant Analytic Hierarchy Process

2011 ◽  
Vol 219-220 ◽  
pp. 112-115
Author(s):  
Zun Qi Yang ◽  
Hai Lin
Keyword(s):  
Discriminant Analysis ◽  
Analytic Hierarchy Process ◽  
Linear Discriminant Analysis ◽  
Discriminant Function ◽  
Linear Discriminant Function ◽  
Simulation Test ◽  
Analytic Hierarchy ◽  
Linear Discriminant ◽  
Hierarchy Process

The paper gives the linear discriminant function (LDF) based on the theory of linear discriminant analysis (LDA) combined with computing weights of the different indicators by analytic hierarchy process (AHP) to predict a new e-commerce customer’s individual credibility level and analyzes the result of a simulation test to justify the theory in this research.

THE CONSTRUCTION OF THE LINEAR DISCRIMINANT ANALYSIS FUNCTION AND ITS EVALUATION TO DETERMINE EXTREME METEOROLOGICAL PHENOMENA (ICE DEPOSITS) IN UKRAINE

2020 ◽  
pp. 82-94
Author(s):  
S.I. Pyasetska ◽  
N.P. Grebenyuk ◽  
S.V Savchuk
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Discriminant Function ◽  
Winter Season ◽  
Correlation Coefficients ◽  
Linear Discriminant Function ◽  
Average Wind Speed ◽  
Linear Discriminant ◽  
Average Wind ◽  
Actual Formation

o predict the possibility of ice deposits on the territory of Ukraine in the winter season, an analogous approach is proposed using the construction of the equations of the linear discriminant function. For this, the correlation coefficients between 13 meteorological values (per day) at the start dates of ice deposits at all stations of Ukraine were calculated. Significant correlation coefficients were determined between individual meteorological variables, such as average air temperature, maximum, minimum average air humidity, average wind speed, and atmospheric pressure at sea level. It is these quantities that were used to construct the equations of the linear discriminant function and for the dates of the actual formation of ice deposits and the further forecast of its formation from a three-day lead time. As a result of the calculations for the winter season of 2001-2013 an equation of the linear discriminant function was obtained for the dates of the actual formation of ice deposits and a sufficiently high justification was obtained. Also, to predict the possible formation of ice deposits with a three-day lead time, a prognostic function of linear discriminant analysis was obtained to determine possible such deposits for the winter season of 2001-2010. On the example of the regional centers, a satisfactory assessment of the justification on an independent material for the winter season of 2011-2016 was obtained. Thus, in constructing linear discriminant functions to determine the possibility of such an adverse event as ice deposition, a number of conclusions were obtained: – The sufficiently high validity of the discriminant functions of extreme meteorological phenomena (ice deposits) for the winter season 2001-2013 was obtained. It ranges from 91 % (for the data set at selected dates with ice deposits) up to 90% (for an array of data at the date of extreme cold ). – A sufficiently high estimate of the validity of the independent material for the winter season 2014-2016 was obtained. It is up to 78 % (for an array of data on extreme cold dates and from 90 % ( for an array of data on selected dates with ice deposits) . – The prognostic function of linear discriminant analysis was obtained to determine possible (with 3-day timeliness) extreme meteorological phenomena (ice) during the winter season 2001-2010, using only meteorological values with statistically significant correlation, namely, the maximum air temperature; average humidity; and average wind speed. – Sufficiently significant and satisfactory validity of the prognostic functions of possible (with 3-day timeliness) extreme meteorological phenomena (ice deposits) for the winter season 2001-2010 was obtained.

scholarly journals APPLICATION OF BOOTSTRAP METHOD ON ESTIMATION OF THE ERROR RATES IN DISCRIMINANT ANALYSIS

2004 ◽  
Vol 3 (2) ◽  
pp. 1
Author(s):  
I W. MANGKU
Keyword(s):  
Discriminant Analysis ◽  
Comparative Studies ◽  
Bootstrap Method ◽  
Error Rates ◽  
Classification Rule ◽  
Survey Study ◽  
Boot Strap

This paper is a survey study on applications of boot- strap methods for estimating the probability of misclassifications in two-groups discriminant analysis. Here we use the linear discrimi- nant function as classification rule. Some comparative studies on the performances of the considered estimators are also discussed.

scholarly journals A Method for the Statistical Selection of a Panel of Rum Tasters

1969 ◽  
Vol 60 (1) ◽  
pp. 105-112
Author(s):  
Calixta S. Torres ◽  
Juan L. Aguiar ◽  
Eleanor F. Gotay
Keyword(s):  
Discriminant Function ◽  
Agricultural Research ◽  
Research Unit ◽  
Latin Squares ◽  
Linear Discriminant Function ◽  
Evaluation Index ◽  
Statistical Techniques ◽  
Linear Discriminant ◽  
Statistical Selection ◽  
Selection Of

Employees of an agricultural research unit were evaluated for selection as rum tasters. Prospects were classified and ranked considering their relative consistency in four organoleptic tests of four rum samples and their evaluation relative to those of an experienced rum taster. Statistical techniques used were variance analysis of Latin squares for the scores of the evaluation of 10 rum attributes and the calculation of a rum evaluation index for each taster using a linear discriminant function.

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