Algebraically explainable controllers: decision trees and support vector machines join forces

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F23%3A00133937" target="_blank" >RIV/00216224:14330/23:00133937 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1007/s10009-023-00716-z" target="_blank" >https://doi.org/10.1007/s10009-023-00716-z</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10009-023-00716-z" target="_blank" >10.1007/s10009-023-00716-z</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Algebraically explainable controllers: decision trees and support vector machines join forces

Popis výsledku v původním jazyce

Recently, decision trees (DT) have been used as an explainable representation of controllers (a.k.a. strategies, policies, schedulers). Although they are often very efficient and produce small and understandable controllers for discrete systems, complex continuous dynamics still pose a challenge. In particular, when the relationships between variables take more complex forms, such as polynomials, they cannot be obtained using the available DT learning procedures. In contrast, support vector machines provide a more powerful representation, capable of discovering many such relationships, but not in an explainable form. Therefore, we suggest to combine the two frameworks to obtain an understandable representation over richer, domain-relevant algebraic predicates. We demonstrate and evaluate the proposed method experimentally on established benchmarks.

Název v anglickém jazyce

Algebraically explainable controllers: decision trees and support vector machines join forces

Popis výsledku anglicky

Recently, decision trees (DT) have been used as an explainable representation of controllers (a.k.a. strategies, policies, schedulers). Although they are often very efficient and produce small and understandable controllers for discrete systems, complex continuous dynamics still pose a challenge. In particular, when the relationships between variables take more complex forms, such as polynomials, they cannot be obtained using the available DT learning procedures. In contrast, support vector machines provide a more powerful representation, capable of discovering many such relationships, but not in an explainable form. Therefore, we suggest to combine the two frameworks to obtain an understandable representation over richer, domain-relevant algebraic predicates. We demonstrate and evaluate the proposed method experimentally on established benchmarks.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

International Journal on Software Tools for Technology Transfer

ISSN

1433-2779

e-ISSN

—

Svazek periodika

25

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

18

Strana od-do

249-266

Kód UT WoS článku

001045591500001

EID výsledku v databázi Scopus

2-s2.0-85167518918