2020

Conférence

IEEE Congress on Evolutionary Computation. pp 1-8

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 mutliple 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 stateof-art algorithms.

@inproceedings{Hammami2020ClassDependent,
author = {Marwa Hammami and Slim Bechikh and Mohamed Makhlouf and Chih-Cheng Hung and Lamjed Ben Said},
title = {Class Dependent Feature Construction as a Bi-level Optimization Problem},
booktitle = {Proceedings of the IEEE Congress on Evolutionary Computation (CEC)},
pages = {1--8},
year = {2020},
doi = {10.1109/CEC48606.2020.9185723},
publisher = {IEEE},
address = {Glasgow, UK}
}