scholarly journals ANALISIS DISKRIMINAN PADA KLASIFIKASI DESA DI KABUPATEN TABANAN MENGGUNAKAN METODE K-FOLD CROSS VALIDATION

2017 ◽  
Vol 6 (2) ◽  
pp. 106 ◽  
Author(s):  
IDA AYU MADE SUPARTINI ◽  
I KOMANG GDE SUKARSA ◽  
I GUSTI AYU MADE SRINADI
Keyword(s):  
Discriminant Analysis ◽  
Covariance Matrix ◽  
Discriminant Function ◽  
Rural Areas ◽  
Urban Areas ◽  
Cross Validation ◽  
Quadratic Discriminant Analysis ◽  
Linear Discriminant ◽  
Quadratic Discriminant Function ◽  
Fold Cross Validation

Tabanan Regency is one of the eight regencies and one municipality in Bali Province. Administratively, it is divided into 10 districs and  villages. There are rural areas and urban areas in the regions. Discriminant analysis is a technique related to the separation of objects into different groups that have been set previously. The purpose of this research is to classify villlages in Tabanan Regency into urban or rural groups with discriminant analysis. Linear discriminant analysis assumes that the covariance matrix of the two groups are equals, if the assumption of equality of covariance matrix is violated, quadratic discriminant analysis can be used for classification. This research uses k-fold crosss validation method for calculating the accuracy of quadratic discriminant function where . Quadratic discriminant function is obtained by  with the smallest APER value (). All of classification results are stable and consistence.

Quadratic Discriminant Analysis with Covariance for Stock Delineation and Population Differentiation: A Study of Beaked Redfishes (Sebastes mentella and S. fasciatus)

1985 ◽  
Vol 42 (10) ◽  
pp. 1672-1676 ◽  
Author(s):  
R. K. Misra
Keyword(s):  
Discriminant Analysis ◽  
Fisheries Management ◽  
Discriminant Function ◽  
Population Differentiation ◽  
Quadratic Discriminant Analysis ◽  
Morphometric Data ◽  
Linear Discriminant ◽  
Vital Importance ◽  
Quadratic Discriminant Function ◽  
Sebastes Mentella

Stock delineation is of vital importance in fisheries management programs. Linear discriminant function (LDF) has been employed extensively in population differentiation studies but is of severely restricted usefulness when populations differ in their dispersion matrices. Quadratic discriminant function (QDF) is the appropriate analysis to employ in these situations. Here, I analyzed morphometric data of beaked redfishes (Sebastes mentella and S. fasciatus) by a recently developed conditional QDF.

scholarly journals VALIDATION ASSESSMENTS ON RESAMPLING METHOD IN IMBALANCED BINARY CLASSIFICATION FOR LINEAR DISCRIMINANT ANALYSIS

2020 ◽  
Vol 20 ◽  
Author(s):  
Ahmad Hakiim Jamaluddin ◽  
Nor Idayu Mahat
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Performance Measures ◽  
Cross Validation ◽  
Binary Classification ◽  
Class Imbalance ◽  
True Negative ◽  
Linear Discriminant ◽  
Resampling Method ◽  
Fold Cross Validation

The curse of class imbalance affects the performance of many conventional classification algorithms including linear discriminant analysis (LDA). The data pre-processing approach through some resampling methods such as random oversampling (ROS) and random undersampling (RUS) is one of the treatments to alleviate such curse. Previous studies have attempted to address the effect of a resampling method on the performance of LDA. However, some studies contradicted with each other based on different performance measures as well as validation strategies. This manuscript attempted to shed more light on the effect of a resampling method (ROS or RUS) on the performance of LDA based on true positive rate and true negative rate through five validation strategies, i.e. leave-one-out cross-validation, k-fold cross-validation, repeated k-fold cross-validation, naive bootstrap, and .632+ bootstrap. 100 two-group bivariate normally distributed simulated and four real data sets with severe class imbalance ratio were utilised. The analysis on the location and dispersion statistics of the performance measures was further enlightened on: (i) the effect of a resampling method on the performance of LDA, and (ii) the enhancement in the learning fairness of LDA on objects regardless of sample size, hence reducing the effect of the curse of class imbalance.

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 Validation and analysis of the geographical origin of Angelica sinensis (Oliv.) Diels using multi-element and stable isotopes

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11928
Author(s):  
Shanjia Li ◽  
Hui Wang ◽  
Ling Jin ◽  
James F. White ◽  
Kathryn L. Kingsley ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Stable Isotopes ◽  
Discriminant Analysis ◽  
Industrial Development ◽  
Cross Validation ◽  
Geographical Origin ◽  
Mineral Elements ◽  
Partial Least Square ◽  
Angelica Sinensis ◽  
Linear Discriminant

Background Place of origin is an important factor when determining the quality and authenticity of Angelica sinensis for medicinal use. It is important to trace the origin and confirm the regional characteristics of medicinal products for sustainable industrial development. Effectively tracing and confirming the material’s origin may be accomplished by detecting stable isotopes and mineral elements. Methods We studied 25 A. sinensis samples collected from three main producing areas (Linxia, Gannan, and Dingxi) in southeastern Gansu Province, China, to better identify its origin. We used inductively coupled plasma mass spectrometry (ICP-MS) and stable isotope ratio mass spectrometry (IRMS) to determine eight mineral elements (K, Mg, Ca, Zn, Cu, Mn, Cr, Al) and three stable isotopes (δ13C, δ15N, δ18O). Principal component analysis (PCA), partial least square discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) were used to verify the validity of its geographical origin. Results K, Ca/Al, δ13C, δ15N and δ18O are important elements to distinguish A. sinensis sampled from Linxia, Gannan and Dingxi. We used an unsupervised PCA model to determine the dimensionality reduction of mineral elements and stable isotopes, which could distinguish the A. sinensis from Linxia. However, it could not easily distinguish A. sinensis sampled from Gannan and Dingxi. The supervised PLS-DA and LDA models could effectively distinguish samples taken from all three regions and perform cross-validation. The cross-validation accuracy of PLS-DA using mineral elements and stable isotopes was 84%, which was higher than LDA using mineral elements and stable isotopes. Conclusions The PLS-DA and LDA models provide a theoretical basis for tracing the origin of A. sinensis in three regions (Linxia, Gannan and Dingxi). This is significant for protecting consumers’ health, rights and interests.

scholarly journals Rapid Identification of COVID-19 Severity in CT Scans through Classification of Deep Features

2020 ◽  
Author(s):  
Zekuan Yu ◽  
Xiaohu Li ◽  
Haitao Sun ◽  
Jian Wang ◽  
Tongtong Zhao ◽  
...  
Keyword(s):  
Cross Validation ◽  
Rapid Identification ◽  
Ct Scans ◽  
Accurate Identification ◽  
Linear Discriminant ◽  
Holdout Validation ◽  
Novel Coronavirus ◽  
Leave One Out ◽  
Fold Cross Validation

Abstract Background: To implement the real-time diagnosis of the severity of patients infected with novel coronavirus 2019 (COVID-19) and guide the follow-up therapeutic treatment, We collected chest CT scans of 202 patients diagnosed with the COVID-19 from three hospitals in Anhui Province, China.Methods: A total of 729 2D axial plan slices with 246 severe cases and 483 non-severe cases were employed in this study. Four pre-trained deep models (Inception-V3, ResNet-50, ResNet-101, DenseNet-201) with multiple classifiers (linear discriminant, linear SVM, cubic SVM, KNN and Adaboost decision tree) were applied to identify the severe and non-severe COVID-19 cases. Three validation strategies (holdout validation, 10-fold cross-validation and leave-one-out) are employed to validate the feasibility of proposed pipelines. Results and conclusion: The experimental results demonstrate that classification of the features from pre-trained deep models show the promising application in COVID-19 screening whereas the DenseNet-201 with cubic SVM model achieved the best performance. Specifically, it achieved the highest severity classification accuracy of 95.20% and 95.34% for 10-fold cross-validation and leave-one-out, respectively. The established pipeline was able to achieve a rapid and accurate identification of the severity of COVID-19. This may assist the physicians to make more efficient and reliable decisions.

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.

scholarly journals Raman Spectroscopy Analysis for Optical Diagnosis of Oral Cancer Detection

2019 ◽  
Vol 8 (9) ◽  
pp. 1313 ◽  
Author(s):  
Ming-Jer Jeng ◽  
Mukta Sharma ◽  
Lokesh Sharma ◽  
Ting-Yu Chao ◽  
Shiang-Fu Huang ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Discriminant Analysis ◽  
Oral Cancer ◽  
Cross Validation ◽  
Principal Component ◽  
Beta Carotene ◽  
Invasive Technique ◽  
Protein Amino Acid ◽  
Linear Discriminant ◽  
Model Studies

Raman spectroscopy (RS) is widely used as a non-invasive technique in screening for the diagnosis of oral cancer. The potential of this optical technique for several biomedical applications has been proved. This work studies the efficacy of RS in detecting oral cancer using sub-site-wise differentiation. A total of 80 samples (44 tumor and 36 normal) were cryopreserved from three different sub-sites: The tongue, the buccal mucosa, and the gingiva of the oral mucosa during surgery. Linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) were used with principal component analysis (PCA) to classify the samples and the classifications were validated by leave-one-out-cross-validation (LOOCV) and k-fold cross-validation methods. The normal and tumor tissues were differentiated under the PCA-LDA model with an accuracy of 81.25% (sensitivity: 77.27%, specificity: 86.11%). The PCA-QDA classifier model differentiated these tissues with an accuracy of 87.5% (sensitivity: 90.90%, specificity: 83.33%). The PCA-QDA classifier model outperformed the PCA-LDA-based classifier. The model studies revealed that protein, amino acid, and beta-carotene variations are the main biomolecular difference markers for detecting oral cancer.

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