Abstract
Classification is the most common task in machine learning which aims in categorizing the input to set of known labels. Numerous techniques have evolved over time to improve the performance of classification. Ensemble learning is one such technique which focuses on improving the performance by combining diverse set of learners which work together to provide better stability and accuracy. Ensemble learning is used in various fields including medical data analysis, sentiment analysis and banking data analysis. The proposed work focuses on surveying the techniques used in ensemble learning which covers stacking, boosting and bagging techniques, improvements in the field and challenges addressed in ensemble learning for classification. The motivation is to understand the role of ensemble methods in classification across various fields.
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Harine Rajashree, R., Hariharan, M. (2021). A Study on Ensemble Methods for Classification. In: Gopi, E.S. (eds) Machine Learning, Deep Learning and Computational Intelligence for Wireless Communication. Lecture Notes in Electrical Engineering, vol 749. Springer, Singapore. https://doi.org/10.1007/978-981-16-0289-4_10
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DOI: https://doi.org/10.1007/978-981-16-0289-4_10
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