Adapting a Fuzzy Random Forest for Ordinal Multi-Class Classification

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F22%3A00373648" target="_blank" >RIV/68407700:21730/22:00373648 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.3233/FAIA220336" target="_blank" >https://doi.org/10.3233/FAIA220336</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3233/FAIA220336" target="_blank" >10.3233/FAIA220336</a>

Result language

angličtina

Original language name

Adapting a Fuzzy Random Forest for Ordinal Multi-Class Classification

Original language description

Fuzzy Random Forests are well-known Machine Learning ensemble methods. They combine the outputs of multiple Fuzzy Decision Trees to improve the classification performance. Moreover, they can deal with data uncertainty and imprecision thanks to the use of fuzzy logic. Although many classification tasks are binary, in some situations we face the problem of classifying data into a set of ordered categories. This is a particular case of multi-class classification where the order between the classes is relevant, for example in medical diagnosis to detect the severity of a disease. In this paper, we explain how a binary Fuzzy Random Forest may be adapted to deal with ordinal classification. The work is focused on the prediction stage, not on the construction of the fuzzy trees. When a new instance arrives, the rules activation is done with the usual fuzzy operators, but the aggregation of the outputs given by the different rules and trees has been redefined. In particular, we present a procedure for managing the conflicting cases where different classes are predicted with similar support. The support of the classes is calculated using the OWA operator that permits to model the concept of majority agreement.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

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

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2022

Confidentiality

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

Article name in the collection

Artificial Intelligence Research and Development

ISBN

978-1-64368-327-0

ISSN

0922-6389

e-ISSN

1879-8314

Number of pages

10

Pages from-to

181-190

Publisher name

IOS Press

Place of publication

Oxford

Event location

Sitges

Event date

Oct 19, 2022

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

001176468400029