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

Nuria Mollá, Alejandro Rabasa, Jesús J. Rodríguez-Sala, Joaquín Sánchez-Soriano and Antonio Ferrándiz
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Nuria Mollá: Teralco Solutions Ltd., 03203 Elche, Spain
Alejandro Rabasa: R.I. Centre of Operations Research, Miguel Hernandez University of Elche, 03202 Elche, Spain
Jesús J. Rodríguez-Sala: R.I. Centre of Operations Research, Miguel Hernandez University of Elche, 03202 Elche, Spain
Antonio Ferrándiz: Teralco Solutions Ltd., 03203 Elche, Spain

Mathematics, 2021, vol. 10, issue 1, 1-17

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.

Keywords: data mining methods for data streams; explainable temporal data analysis; classification methods (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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