booktitle = "2018 IEEE Congress on Evolutionary Computation (CEC)",
title = "A Multi-Objective Hybrid Filter-Wrapper Evolutionary
Approach for Feature Construction on High-Dimensional
Data",
year = "2018",
abstract = "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
high-dimensional 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.",