  In any knowledge discovery process the value of extracted knowledge is
directly related to the quality of the data used. Big Data problems, generated
by massive growth in the scale of data observed in recent years, also follow
the same dictate. A common problem affecting data quality is the presence of
noise, particularly in classification problems, where label noise refers to the
incorrect labeling of training instances, and is known to be a very disruptive
feature of data. However, in this Big Data era, the massive growth in the scale
of the data poses a challenge to traditional proposals created to tackle noise,
as they have difficulties coping with such a large amount of data. New
algorithms need to be proposed to treat the noise in Big Data problems,
providing high quality and clean data, also known as Smart Data. In this paper,
two Big Data preprocessing approaches to remove noisy examples are proposed: an
homogeneous ensemble and an heterogeneous ensemble filter, with special
emphasis in their scalability and performance traits. The obtained results show
that these proposals enable the practitioner to efficiently obtain a Smart
Dataset from any Big Data classification problem.
