Learning from imperfect data

Pitoyo Hartono, Shuji Hashimoto

    Research output: Contribution to journalArticle

    12 Citations (Scopus)

    Abstract

    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
    Volume7
    Issue number1
    DOIs
    Publication statusPublished - 2007 Jan

    Fingerprint

    Neural networks
    Supervised learning
    Supervisory personnel
    Experiments

    Keywords

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

    ASJC Scopus subject areas

    • Software
    • Computer Science Applications
    • Artificial Intelligence

    Cite this

    Learning from imperfect data. / Hartono, Pitoyo; Hashimoto, Shuji.

    In: Applied Soft Computing Journal, Vol. 7, No. 1, 01.2007, p. 353-363.

    Research output: Contribution to journalArticle

    Hartono, Pitoyo ; Hashimoto, Shuji. / Learning from imperfect data. In: Applied Soft Computing Journal. 2007 ; Vol. 7, No. 1. pp. 353-363.
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