Analysis in the Presence of Classification Errors

2016 ◽  
pp. 179-194
Author(s):  
Parimal Mukhopadhyay
Keyword(s):  
Classification Errors

scholarly journals Classifications for Proliferative Vitreoretinopathy (PVR): An Analysis of Their Use in Publications over the Last 15 Years

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Salvatore Di Lauro ◽  
Mustafa R. Kadhim ◽  
David G. Charteris ◽  
J. Carlos Pastor
Keyword(s):  
Clinical Practice ◽  
Surgical Treatment ◽  
Proliferative Vitreoretinopathy ◽  
Original Description ◽  
Therapy Outcome ◽  
Outcome Parameters ◽  
Classification Errors ◽  
Before And After ◽  
Pubmed Search ◽  
The Way

Purpose. To evaluate the current and suitable use of current proliferative vitreoretinopathy (PVR) classifications in clinical publications related to treatment.Methods. A PubMed search was undertaken using the term “proliferative vitreoretinopathy therapy”. Outcome parameters were the reported PVR classification and PVR grades. The way the classifications were used in comparison to the original description was analyzed. Classification errors were also included. It was also noted whether classifications were used for comparison before and after pharmacological or surgical treatment.Results. 138 papers were included. 35 of them (25.4%) presented no classification reference or did not use any one. 103 publications (74.6%) used a standardized classification. The updated Retina Society Classification, the first Retina Society Classification, and the Silicone Study Classification were cited in 56.3%, 33.9%, and 3.8% papers, respectively. Furthermore, 3 authors (2.9%) used modified-customized classifications and 4 (3.8%) classification errors were identified. When the updated Retina Society Classification was used, only 10.4% of authors used a full C grade description. Finally, only 2 authors reported PVR grade before and after treatment.Conclusions. Our findings suggest that current classifications are of limited value in clinical practice due to the inconsistent and limited use and that it may be of benefit to produce a revised classification.

scholarly journals Morphometric measurements for sexual dimorphism in the Campolina horse breed

2021 ◽  
Vol 56 ◽  
Author(s):  
Lisia Castro Krebs ◽  
Marina Monteiro de Moraes Santos ◽  
Maria Claudia Siqueira ◽  
Brennda Paula Gonçalves de Araujo ◽  
Leonardo Gomes Oliveira ◽  
...  
Keyword(s):  
Sexual Dimorphism ◽  
Discriminant Analysis ◽  
Sexual Differentiation ◽  
Analysis Of Covariance ◽  
Classification Error ◽  
Horse Breed ◽  
Linear Measurements ◽  
Morphometric Measurements ◽  
Classification Errors ◽  
Angular Measurements

Abstract: The objective of this work was to distinguish the sexual dimorphism of horses of the Campolina breed, by morphometric measurements, and to classify them according to sex, using discriminating functions. Two-hundred and fifteen horses were measured, and 39 morphometric measurements were evaluated. The analysis of covariance and the discriminant analysis were performed. Males were taller and showed a wider chest, a greater scapular-humeral angle, and a larger neck, both in length and circumference. Females had a larger heart girth, wider hips, and a greater opening of the coxal-ground and femorotibial angles. Regarding classification, circumference measurements (85.58%) were more accurate in sexual differentiation than the linear (83.26%) and angular (73.02%) ones. As to classification error, of the total animals measured, 10 to 20% of the females were categorized as males. In addition, 11 to 38% of the males were categorized as females. It can be concluded that of the 39 morphometric measurements evaluated, 22 are responsible for sexual dimorphism in the Campolina horse breed. Circumference and linear measurements provide a more assertive classification to determine sexual dimorphism. Angular measurements show greater classification errors regarding the gender of the horses.

scholarly journals BANKRUPTCY PREDICTION MODEL WITH ZETAc OPTIMAL CUT-OFF SCORE TO CORRECT TYPE I ERRORS

2005 ◽  
Vol 7 (1) ◽  
pp. 41 ◽  
Author(s):  
Mohamad Iwan
Keyword(s):  
Type I Error ◽  
Bankruptcy Prediction ◽  
Type I ◽  
Type Ii ◽  
Type Ii Error ◽  
Prior Probabilities ◽  
Prediction Result ◽  
Optimum Cutting ◽  
Classification Errors ◽  
One Year

This research examines financial ratios that distinguish between bankrupt and non-bankrupt companies and make use of those distinguishing ratios to build a one-year prior to bankruptcy prediction model. This research also calculates how many times the type I error is more costly compared to the type II error. The costs of type I and type II errors (cost of misclassification errors) in conjunction to the calculation of prior probabilities of bankruptcy and non-bankruptcy are used in the calculation of the ZETAc optimal cut-off score. The bankruptcy prediction result using ZETAc optimal cut-off score is compared to the bankruptcy prediction result using a cut-off score which does not consider neither cost of classification errors nor prior probabilities as stated by Hair et al. (1998), and for later purposes will be referred to Hair et al. optimum cutting score. Comparison between the prediction results of both cut-off scores is purported to determine the better cut-off score between the two, so that the prediction result is more conservative and minimizes expected costs, which may occur from classification errors.  This is the first research in Indonesia that incorporates type I and II errors and prior probabilities of bankruptcy and non-bankruptcy in the computation of the cut-off score used in performing bankruptcy prediction. Earlier researches gave the same weight between type I and II errors and prior probabilities of bankruptcy and non-bankruptcy, while this research gives a greater weigh on type I error than that on type II error and prior probability of non-bankruptcy than that on prior probability of bankruptcy.This research has successfully attained the following results: (1) type I error is in fact 59,83 times more costly compared to type II error, (2) 22 ratios distinguish between bankrupt and non-bankrupt groups, (3) 2 financial ratios proved to be effective in predicting bankruptcy, (4) prediction using ZETAc optimal cut-off score predicts more companies filing for bankruptcy within one year compared to prediction using Hair et al. optimum cutting score, (5) Although prediction using Hair et al. optimum cutting score is more accurate, prediction using ZETAc optimal cut-off score proved to be able to minimize cost incurred from classification errors.

scholarly journals Classification Errors in Low-Dispersion Spectroscopy

1979 ◽  
Vol 47 ◽  
pp. 493-495
Author(s):  
A. Bruch ◽  
M. Buescher ◽  
W. Samson ◽  
W. C. Seitter
Keyword(s):  
Graduate Students ◽  
Previous Experience ◽  
Spectral Classification ◽  
Classification Errors ◽  
Low Dispersion

With the two parts of the Bonn Spectral Atlas it was hoped to provide a tool for the improvement of accuracy in low-dispersion spectral classification. Earlier estimates from our spectra by different observers with classification experience indicated a rather small increase in classification errors with decreasing resolution. They amount to 0.17 spectral types per 1000 Å mm-1 increase (larger numbers) in dispersion in the range 200 - 1300 Å mm-1 dispersion added to the 0.1 spectral types which are typical errors at MK classification dispersion near 100 Å mm-1 and 0.56 luminosity classes per 1000 Å mm-1 increase (larger numbers) in dispersion in the range 200 - 1300 Å mm-1 dispersion added to the 0.6 luminosity classes which are typical errors at MK classifications near 100 Å mm-1. In order to improve on the error determinations the three first-named authors, all graduate students with no previous experience in spectral classification, classified independently between 800 and 1300 spectra with each of the three Bonn Atlas dispersions of 240, 645 and 1280 Å mm-1. The classification errors of the three students differ by very small amounts and thus are averaged in the following discussion.

scholarly journals Statistical Stability and Spatial Instability in Mapping Forest Tree Species by Comparing 9 Years of Satellite Image Time Series

2019 ◽  
Vol 11 (21) ◽  
pp. 2512 ◽  
Author(s):  
Nicolas Karasiak ◽  
Jean-François Dejoux ◽  
Mathieu Fauvel ◽  
Jérôme Willm ◽  
Claude Monteil ◽  
...  
Keyword(s):  
Time Series ◽  
Tree Species ◽  
Cross Validation ◽  
Satellite Image ◽  
Support Vector ◽  
Validation Data ◽  
Spatial Instability ◽  
Classification Errors ◽  
Leave One Out ◽  
The Impact

Mapping forest composition using multiseasonal optical time series remains a challenge. Highly contrasted results are reported from one study to another suggesting that drivers of classification errors are still under-explored. We evaluated the performances of single-year Formosat-2 time series to discriminate tree species in temperate forests in France and investigated how predictions vary statistically and spatially across multiple years. Our objective was to better estimate the impact of spatial autocorrelation in the validation data on measurement accuracy and to understand which drivers in the time series are responsible for classification errors. The experiments were based on 10 Formosat-2 image time series irregularly acquired during the seasonal vegetation cycle from 2006 to 2014. Due to lot of clouds in the year 2006, an alternative 2006 time series using only cloud-free images has been added. Thirteen tree species were classified in each single-year dataset based on the Support Vector Machine (SVM) algorithm. The performances were assessed using a spatial leave-one-out cross validation (SLOO-CV) strategy, thereby guaranteeing full independence of the validation samples, and compared with standard non-spatial leave-one-out cross-validation (LOO-CV). The results show relatively close statistical performances from one year to the next despite the differences between the annual time series. Good agreements between years were observed in monospecific tree plantations of broadleaf species versus high disparity in other forests composed of different species. A strong positive bias in the accuracy assessment (up to 0.4 of Overall Accuracy (OA)) was also found when spatial dependence in the validation data was not removed. Using the SLOO-CV approach, the average OA values per year ranged from 0.48 for 2006 to 0.60 for 2013, which satisfactorily represents the spatial instability of species prediction between years.

scholarly journals On the Accuracy of Spectral Classifications of Main-Sequence Stars

1973 ◽  
Vol 50 ◽  
pp. 52-59
Author(s):  
W. Gliese
Keyword(s):  
Spectral Type ◽  
Main Sequence ◽  
Standard Errors ◽  
Main Sequence Stars ◽  
M Dwarfs ◽  
Spectral Class ◽  
Classification Errors ◽  
Nearby Stars

By examining the observed dispersion in (colour, spectral type) relations, classification errors have been derived from the data of nearby stars. The comparisons of the colour deviations observed in spectral regions of large variations of colour with type with the deviations in regions of small variations give the following standard errors in units of a tenth of a spectral class: For K dwarfs ±0.6 (MK), ±1.2 (Mt. Wilson), ±0.7 (Kuiper); for early M dwarfs ±0.9: (MK), ±0.7 (Mt. Wilson), ±0.5: (Kuiper); and for late M dwarfs ±0.7 (Kuiper).

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