This paper explores widely the data preparation stage within the process of knowledge
discovery and data mining via feature subset selection in the context of two very well-known …

This contribution presents an approach to take advantage of correlation and consistency
measures in attribute subset selection for image recognition. The proposal has been …

This paper tackles problems where attribute selection is not only able to choose a few
features but also to achieve a low performance classification in terms of accuracy compared …

This paper addresses the situation that may happen after the application of feature subset
selection in terms of a reduced number of selected features or even same solutions obtained …

This paper explores the limitation of consistency-based measures in the context of feature
selection. These kinds of filters are not very widespread in large-dimensionality problems …

This paper introduces an approach to feature subset selection which is able to characterise
the attributes of a supervised machine learning problem into two categories: essential and …

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 …

The prediction of credit card default is a critical issue in business and so has been attracting
more and more attention. In this paper, we focus on the research of credit card default …

Feature selection is a key process for supervised learning algorithms. It involves discarding
irrelevant attributes from the training dataset from which the models are derived. One of the …

Features hold the distinctive characteristics and intrinsic values of data. But it's of no use if
the important information and pattern can not be extracted from the data coming from …