scholarly journals Evaluation of sensory panels of consumers of specialty coffee beverages using the boosting method in discriminant analysis

2015 ◽  
Vol 36 (6) ◽  
pp. 3671 ◽  
Author(s):  
Gilberto Rodrigues Liska ◽  
Fortunato Silva de Menezes ◽  
Marcelo Angelo Cirillo ◽  
Flávio Meira Borém ◽  
Ricardo Miguel Cortez ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
False Negative ◽  
False Negative Rate ◽  
Error Rates ◽  
Training Data ◽  
Classification Rule ◽  
Classification Methods ◽  
Training Set ◽  
Linear Discriminant ◽  
Boosting Method

Automatic classification methods have been widely used in numerous situations and the boosting method has become known for use of a classification algorithm, which considers a set of training data and, from that set, constructs a classifier with reweighted versions of the training set. Given this characteristic, the aim of this study is to assess a sensory experiment related to acceptance tests with specialty coffees, with reference to both trained and untrained consumer groups. For the consumer group, four sensory characteristics were evaluated, such as aroma, body, sweetness, and final score, attributed to four types of specialty coffees. In order to obtain a classification rule that discriminates trained and untrained tasters, we used the conventional Fisher’s Linear Discriminant Analysis (LDA) and discriminant analysis via boosting algorithm (AdaBoost). The criteria used in the comparison of the two approaches were sensitivity, specificity, false positive rate, false negative rate, and accuracy of classification methods. Additionally, to evaluate the performance of the classifiers, the success rates and error rates were obtained by Monte Carlo simulation, considering 100 replicas of a random partition of 70% for the training set, and the remaining for the test set. It was concluded that the boosting method applied to discriminant analysis yielded a higher sensitivity rate in regard to the trained panel, at a value of 80.63% and, hence, reduction in the rate of false negatives, at 19.37%. Thus, the boosting method may be used as a means of improving the LDA classifier for discrimination of trained tasters.

Comparison of Supervised Classification Methods for Protein Profiling in Cancer Diagnosis

2007 ◽  
Vol 3 ◽  
pp. 117693510700300 ◽  
Author(s):  
Nadège Dossat ◽  
Alain Mangé ◽  
Jérôme Solassol ◽  
William Jacot ◽  
Ludovic Lhermitte ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Supervised Classification ◽  
Protein Profiling ◽  
Clinical Proteomics ◽  
High Dimensional ◽  
Classification Methods ◽  
Linear Discriminant ◽  
Supervised Classification Methods

A key challenge in clinical proteomics of cancer is the identification of biomarkers that could allow detection, diagnosis and prognosis of the diseases. Recent advances in mass spectrometry and proteomic instrumentations offer unique chance to rapidly identify these markers. These advances pose considerable challenges, similar to those created by microarray-based investigation, for the discovery of pattern of markers from high-dimensional data, specific to each pathologic state (e.g. normal vs cancer). We propose a three-step strategy to select important markers from high-dimensional mass spectrometry data using surface enhanced laser desorption/ionization (SELDI) technology. The first two steps are the selection of the most discriminating biomarkers with a construction of different classifiers. Finally, we compare and validate their performance and robustness using different supervised classification methods such as Support Vector Machine, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Neural Networks, Classification Trees and Boosting Trees. We show that the proposed method is suitable for analysing high-throughput proteomics data and that the combination of logistic regression and Linear Discriminant Analysis outperform other methods tested.

Zur Evolution des deutschen Konjunkturzyklus 1958-2004 / On the Evolution of German Business Cycles 1958-2004

2008 ◽  
Vol 228 (1) ◽  
Author(s):  
Ullrich Heilemann ◽  
Roland Schuhr
Keyword(s):  
Time Series ◽  
Discriminant Analysis ◽  
Business Cycles ◽  
Business Cycle ◽  
Linear Discriminant Analysis ◽  
Turning Point ◽  
Classification Methods ◽  
The West ◽  
Linear Discriminant ◽  
Macroeconomic Variables

SummaryThis paper examines changes of the (West) German business cycle from 1958 to 2004. It starts with a multivariate linear discriminant analysis (LDA) based decomposition of the cycle into 4 phases (upswing, upper turning point, downswing, lower turning point). After examining inter-cyclical changes of the cycle, i.e. changes of the weights of the 12 macroeconomic variables employed for classification, the question of intra-cyclical changes is addressed. This is done by using DLDA, a new dynamic variant of LDA which exploits the time series character of the data used to analyse changes of the multivariate structure of the cycle. The DLDA results exemplify that the transition from one to the next phase is much smoother and more continuous than might be expected. Within the sample examined these movements vary as well as the weights attributed to the classifying variables. In a methodological perspective DLDA turns out to be a promising broadening of classification methods.

The Comparison of Laplacianfaces QR Decomposition and Linear Discriminant Analysis QR Decomposition Algorithm for Face Recognition System on Orthogonal Subspace

2014 ◽  
Vol 71 (1) ◽  
Author(s):  
Purbandini Purbandini
Keyword(s):  
Face Recognition ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Selection Process ◽  
Recognition System ◽  
Qr Decomposition ◽  
Training Data ◽  
Distance Method ◽  
Linear Discriminant ◽  
Face Recognition System

Development of an optimal face recognition system will greatly depend on the characteristics of the selection process are as a basis to pattern recognition. In the characteristic selection process, there are 2 aspects that will be of mutual influence such the reduction of the amount of data used in the classification aspects and increasing discrimination ability aspects. Linear Discriminat Analysis method helps presenting the global structure while Laplacianfaces method is one method that is based on appearance (appearance-based method) in face recognition, in which the local manifold structure presented in the adjacency graph mapped from the training data points. Linear Discriminant Analysis QR decomposition has a computationally low cost because it has small dimensions so that the efficiency and scalability are very high when compared with algorithms of other Linear Discriminant Analysis methods. Laplacianfaces QR decomposition was a algorithm to obtain highly speed and accuracy, and tiny space to keep data on the face recognition. This algorithm consists of 2 stages. The first stage maximizes the distance of between-class scatter matrices by using QR decomposition and the second stage to minimize the distance of within-class scatter matrices. Therefore, it is obtained an optimal discriminant in the data. In this research, classification using the Euclidean distance method. In these experiments using face databases of the Olivetti-Att-ORL, Bern and Yale. The minimum error was achieved with the Laplacianfaces QR decomposition and Linear Discriminant Analysis QR decomposition are 5.88% and 9.08% respectively. 

scholarly journals Automated detection of disulfide bridges in electron density maps using linear discriminant analysis

2005 ◽  
Vol 38 (1) ◽  
pp. 121-125 ◽  
Author(s):  
Thomas R. Ioerger
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Electron Density ◽  
Model Building ◽  
Computational Method ◽  
Training Data ◽  
Decision Threshold ◽  
Disulfide Bridges ◽  
Linear Discriminant ◽  
Density Maps

The ability to recognize disulfide bridges automatically in electron density maps would be useful to both protein crystallographers and automated model-building programs. A computational method is described for recognizing disulfide bridges in uninterpreted maps based on linear discriminant analysis. For each localized spherical region in a map, a vector of rotation-invariant numeric features is calculated that captures various aspects of the local pattern of density. These features values are then input into a linear equation, with coefficients computed to optimize discrimination of a set of training examples (disulfides and non-disulfides), and compared with a decision threshold. The method is shown to be highly accurate at distinguishing disulfides from non-disulfides in both the original training data and in real (experimental) electron density maps of other proteins.

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.

Appropriateness of Linear Discriminant and Multinomial Classification Analysis in Marketing Research

1978 ◽  
Vol 15 (1) ◽  
pp. 103-112 ◽  
Author(s):  
William R. Dillon ◽  
Matthew Goldstein ◽  
Leon G. Schiffman
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Marketing Research ◽  
Classification Problem ◽  
Relative Performance ◽  
New Method ◽  
Classification Methods ◽  
Classification Analysis ◽  
Linear Discriminant ◽  
Usage Behavior

Buyer usage behavior data are used to compare the relative performance of a linear discriminant analysis and several multinomial classification methods. The potential shortcomings of each of the procedures investigated are cited, and a new method for determining the contribution of a variable to discrimination in the context of the multinomial classification problem also is presented.

Detection of Milk Fat Adulteration by Linear Discriminant Analysis of Fatty Acid Data

1994 ◽  
Vol 77 (5) ◽  
pp. 1326-1334 ◽  
Author(s):  
Franz Ulberth
Keyword(s):  
Fatty Acid ◽  
Discriminant Analysis ◽  
Olive Oil ◽  
Linear Discriminant Analysis ◽  
Palm Oil ◽  
Milk Fat ◽  
Classification Rule ◽  
Gas Liquid Chromatography ◽  
Data Set ◽  
Linear Discriminant

Abstract Analysis of the fatty acid (FA) profile of milk fat (MF) by gas-liquid chromatography is widely used to detect adulteration with foreign fats. On the basis of the FA spectra of 352 genuine Austrian MF samples collected over a 4-year period, the effectiveness of concentration ranges of the major FA of MF and of certain FA ratios to identify non-MF/MF mixtures was tested. FA ratios proved useful for the detection of coconut fat in MF and admixture of vegetable oils rich in linoleic acid down to a level of 2%. This approach failed to identify non-MF/MF blends containing beef tallow, lard, olive oil, or palm oil at a level less than 10% commingling. Linear discriminant analysis applied to FA data was successful in distinguishing pure MFfrom adulterated MF. Computer-simulated data were used to derive the discriminant functions. Saturated and un-saturated FA with 18 C atoms were the most useful discriminating variables selected by a stepwise variable selection procedure. More than 95% of a data set composed of pure MF, and non-MF/MF blends containing 3% of either tallow, lard, olive oil, or palm oil were correctly classified. The validity of the classification rule was also tested by 206 gravimetrically prepared fat mixtures. Mixtures containing >3% foreign fat were detected in all cases.

scholarly journals Hyperspectral Reflectance Imaging Combined with Multivariate Analysis for Diagnosis of Sclerotinia Stem Rot on Arabidopsis Thaliana Leaves

2019 ◽  
Vol 9 (10) ◽  
pp. 2092 ◽  
Author(s):  
Jing Liang ◽  
Xiaoli Li ◽  
Panpan Zhu ◽  
Ning Xu ◽  
Yong He
Keyword(s):  
Multivariate Analysis ◽  
False Negative ◽  
False Negative Rate ◽  
Projection Algorithm ◽  
Stem Rot ◽  
Support Vector ◽  
Hyperspectral Reflectance ◽  
Sclerotinia Stem Rot ◽  
K Nearest Neighbor ◽  
Linear Discriminant

Sclerotinia stem rot (SSR) is one of the most destructive diseases in the world caused by Sclerotinia sclerotiorum (S. sclerotiorum), resulting in significant yield loss. Early and high-throughput detection would be critical to prevent SSR from spreading. This study aimed to propose a feasible method for SSR detection based on the hyperspectral imaging coupled with multivariate analysis. The performance of different detecting algorithms were compared by combining the extreme learning machine (ELM), K-nearest neighbor algorithm (KNN), linear discriminant analysis (LDA), naïve Bayes classifier (NB) and the support vector machine (SVM) with the random frog (RF), successive projection algorithm (SPA) and sequential forward selection (SFS). The similarity of selected optimal wavelengths by three different feature selection methods indicated a high correlation between selected wavelengths and SSR. Compared with KNN, LDA, NB, and SVM, three wavelengths (455, 671 and 747 nm) selected by SFS-CA combined with ELM could achieve relatively better results with the overall accuracy of 93.7% and the lowest false negative rate of 2.4%. These results demonstrated the potential of the presented method using hyperspectral reflectance imaging combined with multivariate analysis for SSR diagnosis.

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