Prediction of Bankruptcy with SVM Classifiers Among Retail Business Companies in EU

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43110%2F16%3A43909135" target="_blank" >RIV/62156489:43110/16:43909135 - isvavai.cz</a>

Výsledek na webu

<a href="http://acta.mendelu.cz/media/pdf/actaun_2016064020627.pdf" target="_blank" >http://acta.mendelu.cz/media/pdf/actaun_2016064020627.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.11118/actaun201664020627" target="_blank" >10.11118/actaun201664020627</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Prediction of Bankruptcy with SVM Classifiers Among Retail Business Companies in EU

Popis výsledku v původním jazyce

Article focuses on the prediction of bankruptcy of the 850 medium-sized retail business companies in EU from which 48 companies gone bankrupt in 2014 with respect to lag of the used features. From various types of classifi cation models we chose Support vector machines method with linear, polynomial and radial kernels to acquire best results. Pre-processing is enhanced with fi lter based feature selection like Gain ratio, Chi-square and Relief algorithm to acquire attributes with the best information value. On this basis we deal with random samples of fi nancial data to measure prediction accuracy with the confusion matrices and area under curve values for diff erent kernel types and selected features. From the results it is obvious that with the rising distance to the bankruptcy there drops precision of bankruptcy prediction. The last year (2013) with avaible fi nancial data off ers best total prediction accuracy, thus we also infer both the Error I and II types for better recognizance. The 3rd order polynomial kernel off ers better accuracy for bankruptcy prediction than linear and radial versions. But in terms of the total accuracy we recommend to use radial kernel without feature selection.

Název v anglickém jazyce

Prediction of Bankruptcy with SVM Classifiers Among Retail Business Companies in EU

Popis výsledku anglicky

Article focuses on the prediction of bankruptcy of the 850 medium-sized retail business companies in EU from which 48 companies gone bankrupt in 2014 with respect to lag of the used features. From various types of classifi cation models we chose Support vector machines method with linear, polynomial and radial kernels to acquire best results. Pre-processing is enhanced with fi lter based feature selection like Gain ratio, Chi-square and Relief algorithm to acquire attributes with the best information value. On this basis we deal with random samples of fi nancial data to measure prediction accuracy with the confusion matrices and area under curve values for diff erent kernel types and selected features. From the results it is obvious that with the rising distance to the bankruptcy there drops precision of bankruptcy prediction. The last year (2013) with avaible fi nancial data off ers best total prediction accuracy, thus we also infer both the Error I and II types for better recognizance. The 3rd order polynomial kernel off ers better accuracy for bankruptcy prediction than linear and radial versions. But in terms of the total accuracy we recommend to use radial kernel without feature selection.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

BB - Aplikovaná statistika, operační výzkum

OECD FORD obor

—

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis

ISSN

1211-8516

e-ISSN

—

Svazek periodika

64

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

8

Strana od-do

627-634

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-84969988780