Incremental Decision Rules Algorithm: A Probabilistic and Dynamic Approach to Decisional Data Stream Problems
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) pro...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/10/1/16 |
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| author | Nuria Mollá Alejandro Rabasa Jesús J. Rodríguez-Sala Joaquín Sánchez-Soriano Antonio Ferrándiz |
| author_facet | Nuria Mollá Alejandro Rabasa Jesús J. Rodríguez-Sala Joaquín Sánchez-Soriano Antonio Ferrándiz |
| author_sort | Nuria Mollá |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T03:33:20Z |
| format | Article |
| id | doaj.art-f2d27e59a787438cbda510147f36ec97 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T03:33:20Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-f2d27e59a787438cbda510147f36ec972023-11-23T11:52:55ZengMDPI AGMathematics2227-73902021-12-011011610.3390/math10010016Incremental Decision Rules Algorithm: A Probabilistic and Dynamic Approach to Decisional Data Stream ProblemsNuria Mollá0Alejandro Rabasa1Jesús J. Rodríguez-Sala2Joaquín Sánchez-Soriano3Antonio Ferrándiz4Teralco Solutions Ltd., 03203 Elche, SpainR.I. Centre of Operations Research, Miguel Hernandez University of Elche, 03202 Elche, SpainR.I. Centre of Operations Research, Miguel Hernandez University of Elche, 03202 Elche, SpainR.I. Centre of Operations Research, Miguel Hernandez University of Elche, 03202 Elche, SpainTeralco Solutions Ltd., 03203 Elche, SpainData 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.https://www.mdpi.com/2227-7390/10/1/16data mining methods for data streamsexplainable temporal data analysisclassification methods |
| spellingShingle | Nuria Mollá Alejandro Rabasa Jesús J. Rodríguez-Sala Joaquín Sánchez-Soriano Antonio Ferrándiz Incremental Decision Rules Algorithm: A Probabilistic and Dynamic Approach to Decisional Data Stream Problems Mathematics data mining methods for data streams explainable temporal data analysis classification methods |
| title | Incremental Decision Rules Algorithm: A Probabilistic and Dynamic Approach to Decisional Data Stream Problems |
| title_full | Incremental Decision Rules Algorithm: A Probabilistic and Dynamic Approach to Decisional Data Stream Problems |
| title_fullStr | Incremental Decision Rules Algorithm: A Probabilistic and Dynamic Approach to Decisional Data Stream Problems |
| title_full_unstemmed | Incremental Decision Rules Algorithm: A Probabilistic and Dynamic Approach to Decisional Data Stream Problems |
| title_short | Incremental Decision Rules Algorithm: A Probabilistic and Dynamic Approach to Decisional Data Stream Problems |
| title_sort | incremental decision rules algorithm a probabilistic and dynamic approach to decisional data stream problems |
| topic | data mining methods for data streams explainable temporal data analysis classification methods |
| url | https://www.mdpi.com/2227-7390/10/1/16 |
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