Alternative Ensemble Classifier Based on Penalty Strategy for Improving Prediction Accuracy View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2018-10-17

AUTHORS

Cindy-Pamela Lopez , Maritzol Tenemaza , Edison Loza-Aguirre

ABSTRACT

The Increasing demand for accurate classifier systems for user’s service has called the application of machine learning techniques. One of the most used techniques consist in grouping classifiers into an ensemble classifier. The resulting classifier is generally more accurate than any individual classifier. In this work, we propose an alternative ensemble classification system based on combining three classifiers: Naive Bayes, Random Forest and Multilayer Perceptron. To increase robustness of prediction, we organized the algorithms used by penalty calculations instead of a score-based voting system. We have compared the results of our proposed penalty factor system with the most popular classification algorithms and an ensemble classifier that uses the voting technique. Our results show that our algorithm improves the accuracy in prediction of classification in exchange of a reasonable response time. More... »

PAGES

1070-1076

Book

TITLE

Human Systems Engineering and Design

ISBN

978-3-030-02052-1
978-3-030-02053-8

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-030-02053-8_163

DOI

http://dx.doi.org/10.1007/978-3-030-02053-8_163

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1107669747


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