2018

Conférence

Proceedings of the IEEE Congress on Evolutionary Computation. pp 1-8

Feature selection and construction are important
pre-processing techniques in data mining. They may allow
not only dimensionality reduction but also classifier accuracy
and efficiency improvement. These two techniques are of great
importance especially for the case of high-dimensional data.
Feature construction for high-dimensional data is still a very
challenging topic. This can be explained by the large search space
of feature combinations, whose size is a function of the number of
features. Recently, researchers have used Genetic Programming
(GP) for feature construction and the obtained results were
promising. Unfortunately, the wrapper evaluation of each feature
subset, where a feature can be constructed by a combination
of features, is computationally intensive since such evaluation
requires running the classifier on the data sets. Motivated by
this observation, we propose, in this paper, a hybrid multiobjective evolutionary approach for efficient feature construction
and selection. Our approach uses two filter objectives and one
wrapper objective corresponding to the accuracy. In fact, the
whole population is evaluated using two filter objectives. However,
only non-dominated (best) feature subsets are improved using an
indicator-based local search that optimizes the three objectives
simultaneously. Our approach has been assessed on six highdimensional datasets and compared with two existing prominent
GP approaches, using three different classifiers for accuracy
evaluation. Based on the obtained results, our approach is shown
to provide competitive and better results compared with two
competitor GP algorithms tested in this study

@inproceedings{hammami2018multi,
author = {Marwa Hammami and Slim Bechikh and Chih-Chung Hung and Lamjed Ben Said},
title = {A Multi-objective Hybrid Filter-Wrapper Approach For Feature Construction On High-Dimensional Data Using GP},
booktitle = {Proceedings of the IEEE Congress on Evolutionary Computation (CEC)},
year = {2018},
pages = {1--8},
publisher = {IEEE}
}