Reusing Genetic Programming for Ensemble Selection in Classification of Unbalanced Data
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{Bhowan:2014:ieeeTEC,
-
author = "Urvesh Bhowan and Mark Johnston and Mengjie Zhang and
Xin Yao",
-
title = "Reusing Genetic Programming for Ensemble Selection in
Classification of Unbalanced Data",
-
journal = "IEEE Transactions on Evolutionary Computation",
-
year = "2014",
-
volume = "18",
-
number = "6",
-
pages = "893--908",
-
month = dec,
-
keywords = "genetic algorithms, genetic programming",
-
ISSN = "1089-778X",
-
DOI = "doi:10.1109/TEVC.2013.2293393",
-
size = "16 pages",
-
abstract = "Classification algorithms can suffer from performance
degradation when the class distribution is unbalanced.
This paper develops a two-step approach to evolving
ensembles using genetic programming (GP) for unbalanced
data. The first step uses multi-objective (MO) GP to
evolve a Pareto approximated front of GP classifiers to
form the ensemble by trading-off the minority and the
majority class against each other during learning. The
MO component alleviates the reliance on sampling to
artificially re-balance the data. The second step,
which is the focus this paper, proposes a novel
ensemble selection approach using GP to automatically
find/choose the best individuals for the ensemble. This
new GP approach combines multiple Pareto-approximated
front members into a single composite genetic program
solution to represent the (optimised) ensemble. This
ensemble representation has two main
advantages/novelties over traditional genetic algorithm
(GA) approaches. Firstly, by limiting the depth of the
composite solution trees, we use selection pressure
during evolution to find small highly-cooperative
groups of individuals for the ensemble. This means that
ensemble sizes are not fixed a priori (as in GA), but
vary depending on the strength of the base learners.
Secondly, we compare different function set operators
in the composite solution trees to explore new ways to
aggregate the member outputs and thus, control how the
ensemble computes its output. We show that the proposed
GP approach evolves smaller, more diverse ensembles
compared to an established ensemble selection
algorithm, while still performing as well as, or better
than the established approach. The evolved GP ensembles
also perform well compared to other bagging and
boosting approaches, particularly on tasks with high
levels of class imbalance.",
-
notes = "Also known as \cite{6677603}",
- }
Genetic Programming entries for
Urvesh Bhowan
Mark Johnston
Mengjie Zhang
Xin Yao
Citations