One of the major problems in Big Data is a large number of features or dimensions, which
causes the issue of “the curse of dimensionality” when applying machine learning …

Feature selection is an important task in data mining and machine learning to reduce the
dimensionality of the data and increase the performance of an algorithm, such as a …

Feature selection is a robust technique for data reduction and an essential step in successful
machine learning applications. Different feature selection methods have been introduced in …

For the classification of motor imagery brain-computer interface (BCI) based on
electroencephalography (EEG), appropriate features are crucial to obtain a high …

Attribute reduction (AR) represents a NP-hard problem, and it is be identified as the
problematic issue of pinpointing the least (possible) subset of characteristics taken from the …

This paper presents an Ant Colony Optimization (ACO) approach for feature selection. The
challenge in the feature selection problem is the large search space that exists due to either …

This paper analyses some tools that could be appropriate as teaching resources for
undergraduate or postgraduate levels. A comparison is performed between two machine …

Feature selection has been applied in several areas of science and engineering for a long
time. This kind of pre-processing is almost mandatory in problems with huge amounts of …

Half of the general population experiences a headache during any given year. Medical data
and information in turn provide knowledge based on which physicians make scientific …

This paper presents a novel procedure to apply in a sequential way two data preparation
techniques from a different nature such as data cleansing and feature selection. For the …