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    Professor / David.Powers@flinders.edu.au

    Flinders University, School of Computer Science, Engineering and Mathematics, Adelaide, South Australia
    KUB/Tilburg University, ITK/Institute for Language and Knowledge Technology, Tilburg, Brabant, Holland
    FB Informatik/Faculty of Computer Science, University of Kaiserslautern, Germany
    Telecom Paris/ENST, Paris, France
    Macquarie University, Sydney, NSW, Australia
    UNSW/University of New South Wales, Sydney, NSW, Australia
    Sydney University, Sydney, NSW, Australia
    Cardiff University, Linguistics Department, Cardiff, Wales, UK
    Beijing Municipal Lab for Multimedia & Intelligent Software, Beijing University of Technology, Beijing, China

    Recall & Precision versus The Bookmaker

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    In the evaluation of models, theories, information retrieval systems, learning systems and neural networks we must deal with the ubiquitous contingency matrix of decisions versus events. In general this is manifested as the result matrix for a series of experiments aimed at predicting or labeling a series of events. The classical evaluation techniques come from information retrieval, using recall and precision as measures. These are now applied well beyond this field, but unfortunately they have fundamental flaws, are frequently abused, and can prefer substandard models. This paper proposes a well-principled evaluation technique that better takes into account the negative effect of an incorrect result and is directly quantifiable as the probability that an informed decision was made rather than a random guess.

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    Description

    Title : Recall & Precision versus The Bookmaker
    Author(s) : David M. W. Powers
    Abstract : In the evaluation of models, theories, information retrieval systems, learning systems and neural networks we must deal with the ubiquitous contingency matrix of decisions versus events. In general this is manifested as the result matrix for a series of experiments aimed at predicting or labeling a series of events. The classical evaluation techniques come from information retrieval, using recall and precision as measures. These are now applied well beyond this field, but unfortunately they have fundamental flaws, are frequently abused, and can prefer substandard models. This paper proposes a well-principled evaluation technique that better takes into account the negative effect of an incorrect result and is directly quantifiable as the probability that an informed decision was made rather than a random guess.
    Keywords : Machine Learning, Evaluation, Chance-Corrected Accuracy, Cohen Kappa, Fleiss Kappa, Correlation, Recall, Precision, RoC AUC

    Subject : Evaluation
    Area : Computer Science
    Language : English
    Year : 2003

    Affiliations Flinders University, School of Computer Science, Engineering and Mathematics, Adelaide, South Australia
    Conference_title : International Conference on Cognitive Science

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    David's Peer Evaluation activity

    Trusted by 1
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    Funded by 4
    • Australian Speech Science Infrastructure: An Audio-Video Speech Corpus of Australian English, Grant Number LE0989734 / Year 2009
    • From Talking Heads to Thinking Heads: A Research Platform for Human Communication Science , Grant Number TS0669874 / Year 2006
    • Heterodensity neuroimaging techniques for spatiotemporal identification and localization , Grant Number DP0988686 / Year 2009
    • Enhanced brain and muscle signal separation verified by electrical scalp recordings from paralysed awake humans, Grant Number DP110101473 / Year 2011

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