Application of Sequential Neural Networks to Predict the Approximation Degree of Decision-making Systems

Jarosław Szkoła, Piotr Artiemjew

2022

Abstract

The paradigm of granular computing appeared from an idea proposed by L. A Zadeh, who assumed that a key element of data mining techniques is the grouping of objects using similarity measures. He assumed that similar objects could have similar decision classes. This assumption also guides other scientific streams such as reasoning by analogy, nearest neighbour method, and rough set methods. This assumption leads to the implication that grouped data, (granules) can be used to reduce the volume of decision systems while preserving their classification efficiency - internal knowledge. This hypothesis has been verified in practice - including in the works of Polkowski and Artiemjew (2007 - 2015) - where they use rough inclusions proposed by Polkowski and Skowron as an approximation tool - using the approximation scheme proposed by Polkowski. In this work, we present the application of sequential neural networks to estimate the degree of approximation of decision systems (the degree of reduction in the size) based on the degree of indiscernibility of the decision system. We use the standard granulation method as a reference method. Pre-estimation of the degree of approximation is an important problem for the considered techniques, in the context of the possibility of their rapid application. This estimation allows the selection of optimal parameters without the need for a classification process.

Paper Citation

in Harvard Style

Szkoła J. and Artiemjew P. (2022). Application of Sequential Neural Networks to Predict the Approximation Degree of Decision-making Systems. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, ISBN 978-989-758-547-0, pages 984-989. DOI: 10.5220/0010992300003116

in Bibtex Style

@conference{icaart22,
author={Jarosław Szkoła and Piotr Artiemjew},
title={Application of Sequential Neural Networks to Predict the Approximation Degree of Decision-making Systems},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,},
year={2022},
pages={984-989},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010992300003116},
isbn={978-989-758-547-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,
TI - Application of Sequential Neural Networks to Predict the Approximation Degree of Decision-making Systems
SN - 978-989-758-547-0
AU - Szkoła J.
AU - Artiemjew P.
PY - 2022
SP - 984
EP - 989
DO - 10.5220/0010992300003116