Synthetic data generator for testing of classification rule algorithms

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F26441021%3A_____%2F17%3AN0000015" target="_blank" >RIV/26441021:_____/17:N0000015 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.nnw.cz/doi/2017/NNW.2017.27.010.pdf" target="_blank" >http://www.nnw.cz/doi/2017/NNW.2017.27.010.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.14311/NNW.2017.27.010" target="_blank" >10.14311/NNW.2017.27.010</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Synthetic data generator for testing of classification rule algorithms

Popis výsledku v původním jazyce

We developed a data generating system that is able to create systematically testing datasets that accomplish user’s requirements such as number of rows, number and type of attributes, number of missing values, class noise and imbalance ratio. These datasets can be used for testing of the algorithms designed for solving classification rule problem. We used them for optimizing of the parameters of the classification algorithm based on the behavior of ant colonies. But they can be advantageously used for other applications too. Program generates output files in ARFF format. Two standards and one user-define probability distributions are used in data generation: uniform distribution, normal distribution and irregular distribution for nominal attributes. To our knowledge, our system is probably the first synthetic data generation system that systematically generates datasets for examination and judgment of the classification rule algorithms.

Název v anglickém jazyce

Synthetic data generator for testing of classification rule algorithms

Popis výsledku anglicky

We developed a data generating system that is able to create systematically testing datasets that accomplish user’s requirements such as number of rows, number and type of attributes, number of missing values, class noise and imbalance ratio. These datasets can be used for testing of the algorithms designed for solving classification rule problem. We used them for optimizing of the parameters of the classification algorithm based on the behavior of ant colonies. But they can be advantageously used for other applications too. Program generates output files in ARFF format. Two standards and one user-define probability distributions are used in data generation: uniform distribution, normal distribution and irregular distribution for nominal attributes. To our knowledge, our system is probably the first synthetic data generation system that systematically generates datasets for examination and judgment of the classification rule algorithms.

Druh

J_ost - Ostatní články v recenzovaných periodicích

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

—

Návaznosti

N - Vyzkumna aktivita podporovana z neverejnych zdroju

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název periodika

Neural Network World

ISSN

2336-4335

e-ISSN

—

Svazek periodika

—

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

14

Strana od-do

215-229

Kód UT WoS článku

—

EID výsledku v databázi Scopus

—