Abstract
Supervised machine learning is a vast field, and meta-learning is one the sub parts of that which is applied on data of previous years. Numerous data mining problems and techniques are available to take out unseen information from huge databases. Different input data are classified into target classes through classification assignment. We are using various machine learning algorithms in our model. Our overall classification contributed by output of base classifier which are under meta-learning. There are many challenges in the real world problem can be explained by combining machine learning algorithm. Meta-learning algorithms like bagging, dabbing, rotation forest, filtered classifier and decorate are widely used. Meta-learning is based on the principle from preceding experience and learns to enhance data mining work. In our paper, we investigate the performance of bagging, logitboost and filtered classifiers on a biomedical dataset by some popular learning algorithms for meta-data approaches.
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Baitharu, T.R., Bharti, P.K., Pani, S.K. (2022). Biomedical Data Classification Using Meta-learning: An Experimental Investigation. In: Borah, S., Mishra, S.K., Mishra, B.K., Balas, V.E., Polkowski, Z. (eds) Advances in Data Science and Management . Lecture Notes on Data Engineering and Communications Technologies, vol 86. Springer, Singapore. https://doi.org/10.1007/978-981-16-5685-9_41
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DOI: https://doi.org/10.1007/978-981-16-5685-9_41
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