Selective negative correlation learning approach to incremental learning

Ke TANG, Minlong LIN, Fernanda L. MINKU, Xin YAO

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

25 Citations (Scopus)


Negative correlation learning (NCL) is a successful approach to constructing neural network ensembles. In batch learning mode, NCL outperforms many other ensemble learning approaches. Recently, NCL has also shown to be a potentially powerful approach to incremental learning, while the advantages of NCL have not yet been fully exploited. In this paper, we propose a selective NCL (SNCL) algorithm for incremental learning. Concretely, every time a new training data set is presented, the previously trained neural network ensemble is cloned. Then the cloned ensemble is trained on the new data set. After that, the new ensemble is combined with the previous ensemble and a selection process is applied to prune the whole ensemble to a fixed size. This paper is an extended version of our preliminary paper on SNCL. Compared to the previous work, this paper presents a deeper investigation into SNCL, considering different objective functions for the selection process and comparing SNCL to other NCL-based incremental learning algorithms on two more real world bioinformatics data sets. Experimental results demonstrate the advantage of SNCL. Further, comparisons between SNCL and other existing incremental learning algorithms, such Learn + + and ARTMAP, are also presented. © 2009 Elsevier B.V.
Original languageEnglish
Pages (from-to)2796-2805
Number of pages10
Issue number13-15
Early online date17 Apr 2009
Publication statusPublished - Aug 2009
Externally publishedYes

Bibliographical note

This work is partially supported by two National Natural Science Foundation of China Grants (nos. 60802036 and U0835002) and the Fund for Foreign Scholars in University Research and Teaching Programs (Grant no. B07033).


  • Incremental learning
  • Negative correlation learning
  • Neural network ensemble
  • Selective ensemble


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