Titlebook: Big Data Preprocessing; Enabling Smart Data Julián Luengo,Diego García-Gil,Francisco Herrera Book 2020 Springer Nature Switzerland AG 2020

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Dimensionality Reduction for Big Data,arises as a possible solution to enable large-scale learning with millions of dimensions. Nevertheless, as any other family of algorithms, reduction methods require an upgrade in its design so that they can work with such magnitudes. Particularly, they must be prepared to tackle the explosive combin

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Data Reduction for Big Data,pace and better define the decision boundaries between classes. Theoretically, reduction techniques should enable the application of learning algorithms on large-scale problems. Nevertheless, standard algorithms suffer from the increment on size and complexity of today’s problems. The objective of t

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Big Data Discretization, a wide range of machine learning methods. With the advent of Big Data, a new wave of large-scale datasets with predominance of continuous features have arrived to industry and academia. However, standard discretizers do not respond well to huge sets of continuous points, and novel distributed discr

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Final Thoughts: From Big Data to Smart Data,nowledge and insights we can extract from it. Referring to the well-known “garbage in, garbage out” principle, accumulating vast amounts of raw data will not guarantee quality results, but poor knowledge. In this last chapter we aim to provide a couple of final thoughts on the importance of data pre

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