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    Classification of protein localisation patterns via supervised neural network learning

    Anastasiadis, A.D. and Magoulas, George and Liu, X. (2003) Classification of protein localisation patterns via supervised neural network learning. In: Berthold, M.R. and Lenz, H.-J. and Bradley, E. and Kruse, R. and Borgelt, C. (eds.) Advances in Intelligent Data Analysis V: 5th International Symposium on Intelligent Data Analysis. Lecture Notes in Computer Science 2810. Springer, pp. 430-439. ISBN 9783540452317.

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    Abstract

    There are so many existing classification methods from diverse fields including statistics, machine learning and pattern recognition. New methods have been invented constantly that claim superior performance over classical methods. It has become increasingly difficult for practitioners to choose the right kind of the methods for their applications. So this paper is not about the suggestion of another classification algorithm, but rather about conveying the message that some existing algorithms, if properly used, can lead to better solutions to some of the challenging real-world problems. This paper will look at some important problems in bioinformatics for which the best solutions were known and shows that improvement over those solutions can be achieved with a form of feed-forward neural networks by applying more advanced schemes for network supervised learning. The results are evaluated against those from other commonly used classifiers, such as the K nearest neighbours using cross validation, and their statistical significance is assessed using the nonparametric Wilcoxon test.

    Metadata

    Item Type: Book Section
    School: School of Business, Economics & Informatics > Computer Science and Information Systems
    Depositing User: Sarah Hall
    Date Deposited: 29 Jun 2021 14:10
    Last Modified: 29 Jun 2021 14:10
    URI: https://eprints.bbk.ac.uk/id/eprint/44915

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