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Erschienen in:
A Hybrid of Simple Constrained Artificial Bee Colony Algorithm and Flux Balance Analysis for Enhancing Lactate and Succinate in Escherichia Coli Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

A Genetic Programming Approach Applied to Feature Selection from Medical Data

verfasst von : José A. Castellanos-Garzón, Juan Ramos, Yeray Mezquita Martín, Juan F. de Paz, Ernesto Costa

Erschienen in: Practical Applications of Computational Biology and Bioinformatics, 12th International Conference

Verlag: Springer International Publishing

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Abstract

Genetic programming represents a flexible and powerful evolutionary technique in machine learning. The use of genetic programming for rule induction has generated interesting results in classification problems. This paper proposes an evolutionary approach for logical rule induction, which is applied to clinical data. Since logical rules disclose knowledge from the analyzed data, we use such a knowledge to filter features from the target dataset. The results reached by the used dataset have been very promising when used in classification tasks and compared with other methods.

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Vorheriges Kapitel Metabolite Integration Pipeline for the Improvement of Human Metabolic Models
Nächstes Kapitel A DNA Sequence Corpus for Compression Benchmark
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Metadaten
Titel
A Genetic Programming Approach Applied to Feature Selection from Medical Data
verfasst von
José A. Castellanos-Garzón
Juan Ramos
Yeray Mezquita Martín
Juan F. de Paz
Ernesto Costa
Copyright-Jahr
2019
Buch
Practical Applications of Computational Biology and Bioinformatics, 12th International Conference

Print ISBN: 978-3-319-98701-9

Electronic ISBN: 978-3-319-98702-6

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-319-98702-6_24