Evolving diverse ensembles using genetic programming for classification with unbalanced data

Urvesh BHOWAN, Mark JOHNSTON, Mengjie ZHANG, Xin YAO

Research output: Journal PublicationsJournal Article (refereed)peer-review

174 Citations (Scopus)


In classification, machine learning algorithms can suffer a performance bias when data sets are unbalanced. Data sets are unbalanced when at least one class is represented by only a small number of training examples (called the minority class), while the other class(es) make up the majority. In this scenario, classifiers can have good accuracy on the majority class, but very poor accuracy on the minority class(es). This paper proposes a multiobjective genetic programming (MOGP) approach to evolving accurate and diverse ensembles of genetic program classifiers with good performance on both the minority and majority of classes. The evolved ensembles comprise of nondominated solutions in the population where individual members vote on class membership. This paper evaluates the effectiveness of two popular Pareto-based fitness strategies in the MOGP algorithm (SPEA2 and NSGAII), and investigates techniques to encourage diversity between solutions in the evolved ensembles. Experimental results on six (binary) class imbalance problems show that the evolved ensembles outperform their individual members, as well as single-predictor methods such as canonical GP, naive Bayes, and support vector machines, on highly unbalanced tasks. This highlights the importance of developing an effective fitness evaluation strategy in the underlying MOGP algorithm to evolve good ensemble members. © 1997-2012 IEEE.
Original languageEnglish
Article number6198882
Pages (from-to)368-386
Number of pages19
JournalIEEE Transactions on Evolutionary Computation
Issue number3
Early online date24 May 2013
Publication statusPublished - Jun 2013
Externally publishedYes


  • class imbalance learning
  • Classification
  • genetic programming (GP)
  • multiobjective machine learning (ML)


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