Learning from imperfect data

Pitoyo Hartono*, Shuji Hashimoto

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    14 Citations (Scopus)


    For a supervised learning method, the quality of the training data or the training supervisor is very important in generating reliable neural networks. However, for real world problems, it is not always easy to obtain high quality training data sets. In this research, we propose a learning method for a neural network ensemble model that can be trained with an imperfect training data set, which is a data set containing erroneous training samples. With a competitive training mechanism, the ensemble is able to exclude erroneous samples from the training process, thus generating a reliable neural network. Through the experiment, we show that the proposed model is able to tolerate the existence of erroneous training samples in generating a reliable neural network. The ability of the neural network to tolerate the existence of erroneous samples in the training data lessens the costly task of analyzing and arranging the training data, thus increasing the usability of the neural networks for real world problems.

    Original languageEnglish
    Pages (from-to)353-363
    Number of pages11
    JournalApplied Soft Computing Journal
    Issue number1
    Publication statusPublished - 2007 Jan


    • Competitive learning
    • Imperfect supervisor
    • Neural network ensemble
    • Temperature

    ASJC Scopus subject areas

    • Software
    • Computer Science Applications
    • Artificial Intelligence


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