abstract = "Feature selection and construction are important
pre-processing techniques in data mining. They allow
not only dimensionality reduction but also
classification accuracy and efficiency improvement.
While feature selection consists in selecting a subset
of relevant features from the original feature set,
feature construction corresponds to the generation of
new high-level features, called constructed features,
where each one of them is a combination of a subset of
original features. However, different features can have
different abilities to distinguish different classes.
Therefore, it may be more difficult to construct a
better discriminating feature when combining features
that are relevant to different classes. Based on these
definitions, feature construction could be seen as a
BLOP (Bi-Level optimization Problem) where the feature
subset should be defined in the upper level and the
feature construction is applied in the lower level by
performing multiple followers, each of which generates
a set class dependent constructed features. In this
paper, we propose a new bi-level evolutionary approach
for feature construction called BCDFC that constructs
multiple features which focuses on distinguishing one
class from other classes using Genetic Programming
(GP). A detailed experimental study has been conducted
on six high-dimensional datasets. The statistical
analysis of the obtained results shows the
competitiveness and the outperformance of our bi-level
feature construction approach with respect to many
state-of-art algorithms.",
notes = "SMART lab, University of Tunis, ISG, Tunis,
Tunisia.