EMiner: A Tool for Selecting Classification Algorithms and Optimal Parameters



Document title: EMiner: A Tool for Selecting Classification Algorithms and Optimal Parameters
Journal: Polibits
Database: PERIÓDICA
System number: 000402968
ISSN: 1870-9044
Authors: 1
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Institutions: 1Universidade Federal do Maranhao, Sao Luis, Maranhao. Brasil
Year:
Season: Jul-Dic
Number: 52
Pages: 17-24
Country: México
Language: Inglés
Document type: Artículo
Approach: Analítico
English abstract In this paper, Genetic Algorithm (GA) is used to search for combinations of learning algorithms and associated parameters with maximum accuracy. An important feature of the approach is that the GA initial population is formed by using parameter values gathered from ExpDB (a public database of data mining experiments). The proposed approach was implemented in a tool called EMiner, built on top of a grid based software infrastructure for developing collaborative applications in medicine and healthcare domains (ECADeG project). Experiments on 16 datasets from the UCI repository were performed. The results obtained have shown that the strategy of combining the data from ExpDB via GA is effective in finding classification models with good accuracy
Disciplines: Ciencias de la computación,
Medicina
Keyword: Inteligencia artificial,
Diagnóstico,
Algoritmos genéticos,
Minería de datos
Keyword: Computer science,
Medicine,
Artificial intelligence,
Diagnosis,
Genetic algorithms,
Data mining
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