Z-Regression for Аnalysis and Reliable Prediction of Qualitative Characteristics

A regression model under Z-information has developed in the paper. The initial information for constructing the model is linguistic Z-numbers, defined on the unit segment. For a Z-number, an interval type-2 fuzzy number is defined, using aggregating characteristics of reliability (second component of Z-number). To do this, the aggregating segment of reliability and its middle are use. In the paper was made a choice in favor of transforming Z-numbers into type-2 fuzzy numbers, since Z-numbers are closer in structure to them than to type-1 fuzzy numbers, which, however, are most often used to transform Z-numbers and construct different models. Input and output interval type-2 fuzzy numbers are determined for the initial Z-numbers. The optimization function is defined as the sum of squares for the differences between parameters of output type-2 fuzzy numbers and the model type-2 fuzzy numbers. To determine the unknown regression coefficients, the minimum of the optimization function was found using known methods. Reliability recognition of model output information has developed.

Authors

Institutions

Publication Details

Published
2025-04-01
DOI
https://doi.org/10.63550/iceip.2025.1.1.014
Primary Topic
Machine Learning and ELM
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Z-Regression for Аnalysis and Reliable Prediction of Qualitative Characteristics

Olga Poleshchuk
Machine Learning and ELM
article

Z-Regression for Аnalysis and Reliable Prediction of Qualitative Characteristics

Olga Poleshchuk
article en

Abstract

A regression model under Z-information has developed in the paper. The initial information for constructing the model is linguistic Z-numbers, defined on the unit segment. For a Z-number, an interval type-2 fuzzy number is defined, using aggregating characteristics of reliability (second component of Z-number). To do this, the aggregating segment of reliability and its middle are use. In the paper was made a choice in favor of transforming Z-numbers into type-2 fuzzy numbers, since Z-numbers are closer in structure to them than to type-1 fuzzy numbers, which, however, are most often used to transform Z-numbers and construct different models. Input and output interval type-2 fuzzy numbers are determined for the initial Z-numbers. The optimization function is defined as the sum of squares for the differences between parameters of output type-2 fuzzy numbers and the model type-2 fuzzy numbers. To determine the unknown regression coefficients, the minimum of the optimization function was found using known methods. Reliability recognition of model output information has developed.

Bauman Moscow State Technical University (RU)
Openalex Percentile: Top 95%
Machine Learning and ELM
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Z-Regression for Аnalysis and Reliable Prediction of Qualitative Characteristics — Olga Poleshchuk · (2025) | TGRS Research Map | TGRS