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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Pandey, M. (2018). An Ensemble-Based Approach for the Development of DSS. In: Bhateja, V., Nguyen, B., Nguyen, N., Satapathy, S., Le, DN. (eds) Information Systems Design and Intelligent Applications. Advances in Intelligent Systems and Computing, vol 672. Springer, Singapore. https://doi.org/10.1007/978-981-10-7512-4_39
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DOI: https://doi.org/10.1007/978-981-10-7512-4_39
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