Abstract Machine learning is most often expensive in terms of computational and memory
costs due to training with large volumes of data. Current computational limitations of many …

This paper addresses the effect of data reduction on speeding up training while keeping or
improving the accuracy performance of classification. Since many studies have focused on …

This paper addresses the effect of data reduction on speeding up training while keeping or
improving the accuracy performance of classification. Since many studies have focused on …

Feature engineering is the task of improving predictive modelling performance on a dataset
by transforming its feature space. Existing approaches to automate this process rely on …

Although large datasets are often desirable in machine learning, they slow down training
and require large storage. Data reduction can be used without affecting prediction accuracy …

Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion:
Evolutionary Computation for Feature Selec Page 1 1 Evolutionary Computation for Feature …

The tremendous improvements in techniques for collecting, storing and transferring large
volumes of data have also increased the volume of data for knowledge discovery and data …

Scalable feature selection algorithms should remove irrelevant and redundant features and
scale well on very large datasets. We identify that the currently best state-of-art methods …

Big data are produced in high volume, velocity, veracity, and variety. They present
unprecedented opportunities to improve our life that is deeply rooted in the use of …

Attribute reduction is a crucial issue of rough set theory and has been applied to various
fields. It aims to remove useless or redundant features from data and extract precise rules …