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https://hdl.handle.net/10316/106647
Título: | A Genetic Programming Strategy to Induce Logical Rules for Clinical Data Analysis | Autor: | Castellanos-Garzón, José A. Mezquita Martín, Yeray Jaimes Sánchez, José Luis López García, Santiago Manuel Costa, Ernesto |
Palavras-chave: | clinical data; feature selection; genetic programming; machine learning; data mining; evolutionary computation | Data: | 2020 | Editora: | MDPI | Projeto: | MINISTERIO DE CIENCIA E INNOVACIÓN, Project: La desigualdad económica en la España contemporánea y sus efectos en los mercados, las empresas y el acceso a los recursos naturales y la tierra, Grant No. HAR2016-75010-R, iCIS project (CENTRO-07-ST24-FEDER-002003), which has been co-financed by QREN, in the scope of the Mais Centro Program and European Union’s FEDER project “Intelligent and sustainable mobility supported by multi-agent systems and edge computing (InEDGEMobility): Towards Sustainable Intelligent Mobility: Blockchain-based framework for IoT Security”, Reference: RTI2018-095390-B-C32, financed by the Spanish Ministry of Science, Innovation and Universities (MCIU), the State Research Agency (AEI) and the European Regional Development Fund (FEDER) |
Título da revista, periódico, livro ou evento: | Processes | Volume: | 8 | Número: | 12 | Resumo: | 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: | https://hdl.handle.net/10316/106647 | ISSN: | 2227-9717 | DOI: | 10.3390/pr8121565 | Direitos: | openAccess |
Aparece nas coleções: | I&D CISUC - Artigos em Revistas Internacionais |
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Ficheiro | Descrição | Tamanho | Formato | |
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A-genetic-programming-strategy-to-induce-logical-rules-for-clinical-data-analysisProcesses.pdf | 7.07 MB | Adobe PDF | Ver/Abrir |
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