Abstract
The goal of machine learning algorithm is to understand the basic features of a complex system. If the dataset is large and the number of features is large as well, it is possible that one can get features or input variables easily identified. In case the dataset is small, there may be a circumstance that one may miss some of the observations and then eventually ignore some features and find the minimal set of features to define the system adequately. In this chapter we will cover the feature selection methods that choose a subset of important features and skip the rest and the feature extraction methods that form the minimally accepted feature from original set of features.
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Ghosh, S., Dasgupta, R. (2022). Dimensionality Reduction Methods in Machine Learning. In: Machine Learning in Biological Sciences. Springer, Singapore. https://doi.org/10.1007/978-981-16-8881-2_7
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DOI: https://doi.org/10.1007/978-981-16-8881-2_7
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