Improve performance and robustness of knowledge-based FUZZY LOGIC habitat models

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60076658%3A12310%2F21%3A43903320" target="_blank" >RIV/60076658:12310/21:43903320 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/60077344:_____/21:00546048

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S136481522100181X?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S136481522100181X?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.envsoft.2021.105138" target="_blank" >10.1016/j.envsoft.2021.105138</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Improve performance and robustness of knowledge-based FUZZY LOGIC habitat models

Popis výsledku v původním jazyce

Previous criticisms of knowledge-based fuzzy logic modelling have identified some of its limitations and revealed weaknesses regarding the development of fuzzy sets, the integration of expert knowledge, and the outcomes of different defuzzification processes. We show here how expert disagreement and fuzzy logic mechanisms associated with the rule development and combinations can positively or adversely affect model performance and the interpretation of results. We highlight how expert disagreement can induce uncertainty into model outputs when defining fuzzy sets and selecting a defuzzification method. We present a framework to account for sources of error and bias and improve the performance and robustness of knowledge-based fuzzy logic models. We recommend to 1) provide clear/unambiguous instructions on model development, processes and objectives, including the definition of input variables and fuzzy sets, 2) incorporate the disagreement among experts into the analysis, 3) increase the use of short rules and the OR operator to reduce complexity, and 4) improve model performance and robustness by using narrow fuzzy sets for extreme values of input variables to expand the universe of discourse adequately. Our framework is focused on fuzzy logic models but can be applied to all knowledge-based models that require expert judgment, including expert systems, decision trees and (fuzzy) Bayesian inference systems.

Název v anglickém jazyce

Improve performance and robustness of knowledge-based FUZZY LOGIC habitat models

Popis výsledku anglicky

Previous criticisms of knowledge-based fuzzy logic modelling have identified some of its limitations and revealed weaknesses regarding the development of fuzzy sets, the integration of expert knowledge, and the outcomes of different defuzzification processes. We show here how expert disagreement and fuzzy logic mechanisms associated with the rule development and combinations can positively or adversely affect model performance and the interpretation of results. We highlight how expert disagreement can induce uncertainty into model outputs when defining fuzzy sets and selecting a defuzzification method. We present a framework to account for sources of error and bias and improve the performance and robustness of knowledge-based fuzzy logic models. We recommend to 1) provide clear/unambiguous instructions on model development, processes and objectives, including the definition of input variables and fuzzy sets, 2) incorporate the disagreement among experts into the analysis, 3) increase the use of short rules and the OR operator to reduce complexity, and 4) improve model performance and robustness by using narrow fuzzy sets for extreme values of input variables to expand the universe of discourse adequately. Our framework is focused on fuzzy logic models but can be applied to all knowledge-based models that require expert judgment, including expert systems, decision trees and (fuzzy) Bayesian inference systems.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10602 - Biology (theoretical, mathematical, thermal, cryobiology, biological rhythm), Evolutionary biology

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název periodika

Environmental Modelling and Software

ISSN

1364-8152

e-ISSN

—

Svazek periodika

144

Číslo periodika v rámci svazku

OCT 2021

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

9

Strana od-do

—

Kód UT WoS článku

000696669300003

EID výsledku v databázi Scopus

2-s2.0-85111935779