Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/2366
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dc.contributor.authorKasthuriarachchi, K. T. S-
dc.contributor.authorLiyanage, S. R-
dc.date.accessioned2022-05-18T04:47:44Z-
dc.date.available2022-05-18T04:47:44Z-
dc.date.issued2021-07-01-
dc.identifier.citationKasthuriarachchi, K. T., & Liyanage, S. R. (2021). Three-Layer Stacked Generalization Architecture With Simulated Annealing for Optimum Results in Data Mining. International Journal of Artificial Intelligence and Machine Learning (IJAIML), 11(2), 1-27. http://doi.org/10.4018/IJAIML.20210701.oa10en_US
dc.identifier.urihttp://rda.sliit.lk/handle/123456789/2366-
dc.description.abstractThe combination of different machine learning models to a single prediction model usually improves the performance of the data analysis. Stacking ensembles are one of such approaches to build a highperformance classifier that can be applied to various contexts of data mining. This study proposes an enhanced stacking ensemble by collating a few machine learning algorithms with two-layered meta classifications to address the limitations of existing stacking architecture to utilize simulated annealing algorithm to optimize the classifier configuration in order to reach the best prediction accuracy. The proposed method significantly outperformed three general stacking ensembles of two layers that have been executed using the meta classifiers utilized in the proposed architecture. These assessments have been statistically proven at a 95% confidence level. The novel stacking ensemble has also outperformed the existing ensembles named Adaboost algorithm, gradient boosting algorithm, XGBoost classifier, and bagging classifiers as well.en_US
dc.language.isoenen_US
dc.publisherIGI Globalen_US
dc.relation.ispartofseriesInternational Journal of Artificial Intelligence and Machine Learning (IJAIML);Volume 11 Issue 2 Pages 1-27-
dc.subjectClassifieren_US
dc.subjectEnsembleen_US
dc.subjectHyperparameteren_US
dc.subjectSimulated Annealingen_US
dc.subjectStacked Generalizationen_US
dc.titleThree-Layer Stacked Generalization Architecture With Simulated Annealing for Optimum Results in Data Miningen_US
dc.typeArticleen_US
dc.identifier.doi10.4018/IJAIML.20210701.oa10en_US
Appears in Collections:Research Papers - Open Access Research
Research Papers - SLIIT Staff Publications
Research Publications -Dept of Information Technology

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