Incremental Decision Rules Algorithm: A Probabilistic and Dynamic Approach to Decisional Data Stream Problems

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Title: Incremental Decision Rules Algorithm: A Probabilistic and Dynamic Approach to Decisional Data Stream Problems
Authors: Mollá, Nuria | Rabasa, Alejandro | Rodríguez-Sala, Jesús J. | Sánchez-Soriano, Joaquín | Ferrándiz Colmeiro, Antonio
Research Group/s: Undefined
Center, Department or Service: Universidad de Alicante. Departamento de Tecnología Informática y Computación
Keywords: Data mining methods for data streams | Explainable temporal data analysis | Classification methods
Knowledge Area: Arquitectura y Tecnología de Computadores
Issue Date: 21-Dec-2021
Publisher: MDPI
Citation: Mollá N, Rabasa A, Rodríguez-Sala JJ, Sánchez-Soriano J, Ferrándiz A. Incremental Decision Rules Algorithm: A Probabilistic and Dynamic Approach to Decisional Data Stream Problems. Mathematics. 2022; 10(1):16. https://doi.org/10.3390/math10010016
Abstract: Data science is currently one of the most promising fields used to support the decision-making process. Particularly, data streams can give these supportive systems an updated base of knowledge that allows experts to make decisions with updated models. Incremental Decision Rules Algorithm (IDRA) proposes a new incremental decision-rule method based on the classical ID3 approach to generating and updating a rule set. This algorithm is a novel approach designed to fit a Decision Support System (DSS) whose motivation is to give accurate responses in an affordable time for a decision situation. This work includes several experiments that compare IDRA with the classical static but optimized ID3 (CREA) and the adaptive method VFDR. A battery of scenarios with different error types and rates are proposed to compare these three algorithms. IDRA improves the accuracies of VFDR and CREA in most common cases for the simulated data streams used in this work. In particular, the proposed technique has proven to perform better in those scenarios with no error, low noise, or high-impact concept drifts.
Sponsor: This work was supported by grant DIN2018-010101 funded by MCIN/AEI/10.13039/501100011033, Teralco Solutions Ltd, and PROMETEO2021/063 funded by the Generalitat Valenciana. Open Access funding provided by Miguel Hernández University of Elche.
URI: http://hdl.handle.net/10045/120626
ISSN: 2227-7390
DOI: 10.3390/math10010016
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Peer Review: si
Publisher version: https://doi.org/10.3390/math10010016
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