Abstract
As a discipline, Machine Learning has been adopted and leveraged widely by researchers from several domains. There is a huge range of classifiers already available in machine learning and it has kept on growing with the advancement of this field. However, it is very hard to pick the best classifier among the several similar classifiers suitable for any problem. Recent advancement in this field for solving this issue is the Multiple Classifier System (MCS). It comes under the umbrella of ensemble learning and gives comparatively a better and definite result than a single classifier. MCS has two layers—(i) Base layer—contains a number of ML Classifiers appropriate for any specific task—and (ii) Meta Learner Layer—which aggregates the results from base layer classifiers by using techniques, such as Voting and Stacking. However, the job of selecting the appropriate classifiers from various classifiers or from a family of classifiers for a specific classification or prediction task on any dataset is still unraveling. This work emphasizes determining the characteristics of the selection method of base classifiers in the MCS. Moreover, which Meta Learner layer from Stacking and Voting aggregates the better result according to the different sizes of the base classifiers?
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Tomer, V., Caton, S., Kumar, S., Kumar, B. (2020). A Selection Method for Computing the Ensemble Size of Base Classifier in Multiple Classifier System. In: Iyer, B., Rajurkar, A., Gudivada, V. (eds) Applied Computer Vision and Image Processing. Advances in Intelligent Systems and Computing, vol 1155. Springer, Singapore. https://doi.org/10.1007/978-981-15-4029-5_23
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DOI: https://doi.org/10.1007/978-981-15-4029-5_23
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