Regression analysis based on linguistic associations and Perception-based Logical Deduction

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17610%2F17%3AA1701F9F" target="_blank" >RIV/61988987:17610/17:A1701F9F - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.eswa.2016.08.053" target="_blank" >http://dx.doi.org/10.1016/j.eswa.2016.08.053</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.eswa.2016.08.053" target="_blank" >10.1016/j.eswa.2016.08.053</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Regression analysis based on linguistic associations and Perception-based Logical Deduction

Popis výsledku v původním jazyce

We propose a new generalized model of linguistic variables based on fuzzy partition and its subpartitions. We use this new model for mining relationships between linguistic variables (linguistic associations) from a data set. These relationships can be interpreted as fuzzy IF-THEN rules in the implicative fuzzy inference engine, which is an extended version of the implicative inference called Perception-based Logical Deduction. We show that our extension leads to statistically significant improvements with respect to the previous model used with the help of original and successful Perception-based Logical Deduction. We perform the comparison with different measures of rule quality and five datasets. We can obtain improvements in prediction precision while retaining the interpretability of the models. We also compare our method with the classical machine learning methods and obtain a similar quality of precision, which is very encouraging because interpretability usually leads to worse precision.

Název v anglickém jazyce

Regression analysis based on linguistic associations and Perception-based Logical Deduction

Popis výsledku anglicky

We propose a new generalized model of linguistic variables based on fuzzy partition and its subpartitions. We use this new model for mining relationships between linguistic variables (linguistic associations) from a data set. These relationships can be interpreted as fuzzy IF-THEN rules in the implicative fuzzy inference engine, which is an extended version of the implicative inference called Perception-based Logical Deduction. We show that our extension leads to statistically significant improvements with respect to the previous model used with the help of original and successful Perception-based Logical Deduction. We perform the comparison with different measures of rule quality and five datasets. We can obtain improvements in prediction precision while retaining the interpretability of the models. We also compare our method with the classical machine learning methods and obtain a similar quality of precision, which is very encouraging because interpretability usually leads to worse precision.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10101 - Pure mathematics

Projekt

<a href="/cs/project/LQ1602" target="_blank" >LQ1602: IT4Innovations excellence in science</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název periodika

EXPERT SYSTEMS WITH APPLICATIONS

ISSN

0957-4174

e-ISSN

—

Svazek periodika

67

Číslo periodika v rámci svazku

Leden

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

8

Strana od-do

107-114

Kód UT WoS článku

000386861600010

EID výsledku v databázi Scopus

2-s2.0-84989204336