Análisis comparativo de predicción dentro de bases de datos de cáncer: una aplicación de aprendizaje automático

The objective of this paper is to compare the performance of method prediction: i) Logistic regression, ii) K Nearest Neighbor, iii) K-means, iv) Random Forest, v) Support Vector Machine, vi) Linear Discriminant Analysis, vii) Gaussian Naive Bayes viii) Multilayer Perceptron, within a cancer databas...

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Bibliographic Details
Published in:RISTI : Revista Ibérica de Sistemas e Tecnologias de Informação 2019-01 (E17), p.113-122
Main Authors: Martínez-Toro, Gabriel Mauricio, Rico-Bautista, Dewar, Romero-Riaño, Efrén
Format: Article
Language:Spanish
Subjects:
Artificial intelligence
Artificial neural networks
Bayesian analysis
Big Data
Bioinformatics
Cancer
Classification
Discriminant analysis
Gene expression
Machine learning
Multilayer perceptrons
Regression analysis
Support vector machines
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Summary:The objective of this paper is to compare the performance of method prediction: i) Logistic regression, ii) K Nearest Neighbor, iii) K-means, iv) Random Forest, v) Support Vector Machine, vi) Linear Discriminant Analysis, vii) Gaussian Naive Bayes viii) Multilayer Perceptron, within a cancer database. Logistic regression and artificial neural network classification models: A methodology review. Cancer Classification Based on Microarray Gene Expression Data Using Deep Learning. Cancer Classification Based on Microarray Gene Expression Data Using Deep Learning Cancer Classification Based on Microarray Gene Expression Data Using Deep Learning, (February), 1403-1405. https://doi.org/10.1109/CSCI.2016.269 Guyon, Isabelle, W, Eston, J., & Stephen Barnhill.
ISSN:1646-9895