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Título
A Genetic Programming Strategy to Induce Logical Rules for Clinical Data Analysis
Autor(es)
Materia
Clinical data
Feature selection
Genetic programming
Machine learning
Data mining
Evolutionary computation
Clasificación UNESCO
1209.03 Análisis de Datos
2409.02 Ingeniería Genética
Fecha de publicación
2020-11-27
Editor
MDPI
Citación
Castellanos Garzón, J. A., Mezquita Martín, Y. , Jaimes Sánchez, J. L., López García, S. M., & Costa, E. (2020). A genetic programming strategy to induce logical rules for clinical data analysis. Processes, 8(12), 1-23. https://doi.org/10.3390/PR8121565
Resumen
[EN]This paper proposes a machine learning approach dealing with genetic programming to build classifiers through logical rule induction. In this context, we define and test a set of mutation operators across from different clinical datasets to improve the performance of the proposal for each dataset. The use of genetic programming for rule induction has generated interesting results in machine learning problems. Hence, genetic programming represents a flexible and powerful evolutionary technique for automatic generation of classifiers. Since logical rules disclose knowledge from the analyzed data, we use such knowledge to interpret the results and filter the most important features from clinical data as a process of knowledge discovery. The ultimate goal of this proposal is to provide the experts in the data domain with prior knowledge (as a guide) about the structure
of the data and the rules found for each class, especially to track dichotomies and inequality.
The results reached by our proposal on the involved datasets have been very promising when used in classification tasks and compared with other methods.
URI
ISSN
2227-9717
DOI
10.3390/pr8121565
Versión del editor
Colecciones
- HISCYT. Artículos [22]