A Genetic Programming Approach Applied to Feature Selection from Medical Data

被引：1

作者：

Castellanos-Garzon, Jose A. ^{[1
,2
]}

Ramos, Juan ^{[1
]}

Mezquita Martin, Yeray ^{[1
]}

de Paz, Juan F. ^{[1
]}

Costa, Ernesto ^{[2
]}

机构：

[1] Univ Salamanca, Edificio I D i USAL, BISITE Res Grp, IBSAL, C Espejo S-N, Salamanca 37007, Spain

[2] Univ Coimbra, CISUC, ECOS Res Grp, Polo 2 Pinhal Marrocos, P-3030290 Coimbra, Portugal

来源：

PRACTICAL APPLICATIONS OF COMPUTATIONAL BIOLOGY AND BIOINFORMATICS | 2019年 / 803卷

关键词：

Medical data; Feature selection; Genetic programming; Machine learning; Data mining; Evolutionary computation; CLASSIFICATION; ALGORITHMS; SYSTEM;

D O I：

10.1007/978-3-319-98702-6_24

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：200 / 207

页数：8

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