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2018 | OriginalPaper | Chapter

An Ensemble-Based Approach for the Development of DSS

Author : Mrinal Pandey

Published in: Information Systems Design and Intelligent Applications

Publisher: Springer Singapore

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Abstract

A typical classification problem pertaining to DSS can be solved by employing any classification algorithm such as Bayesian classifiers, neural network, decision tree. But, existing single classifier-based predictive modeling has limited scope to provide a generalized solution for different learning contexts. In this paper, an ensemble-based classification approach using voting methodology is proposed for the decision support system. The proposed ensemble-based system combines three heterogeneous classifiers, namely decision tree, K-nearest neighbor, and aggregating one-dependence estimator classifiers using product of probability voting rule. This paper presents a comparative study of the proposed voting algorithm with the other well-known classifiers for 15 standard benchmark datasets and proved that the proposed method achieves better accuracy for most of the datasets.

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Metadata
Title
An Ensemble-Based Approach for the Development of DSS
Author
Mrinal Pandey
Copyright Year
2018
Publisher
Springer Singapore
Book
Information Systems Design and Intelligent Applications

Print ISBN: 978-981-10-7511-7

Electronic ISBN: 978-981-10-7512-4

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-981-10-7512-4_39

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