Online prediction of chess match result

Mohammad M. Masud, Ameera Al-Shehhi, Eiman Al-Shamsi, Shamma Al-Hassani, Asmaa Al-Hamoudi, Latifur Khan

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    1 Citation (Scopus)

    Abstract

    In this work we propose a framework for predicting chess match outcome while the game is in progress. We make this prediction by examining the moves made by the players. For this purpose, we propose a novel ensemble based learning technique where a profile-based segmentation is done on the training dataset, and one classifier is trained from each such segment. Then the ensemble of classifiers is used to predict the outcome of new chess matches. When a new game is being played this ensemble model is used to dynamically predict the probabilities of white winning, black winning, and drawing after every move. We have evaluated our system with different base learning techniques as well as with different types of features and applied our technique on a large corpus of real chess matches, achieving higher prediction accuracies than traditional classification techniques. We have achieved prediction accuracies close to 66% and most of the correct predictions were made with nine or more moves before the game ended. We believe that this work will motivate the development of online prediction systems for other games, such as other board games and even some field games.

    Original languageEnglish
    Title of host publicationAdvances in Knowledge Discovery and Data Mining - 19th Pacific-Asia Conference, PAKDD 2015, Proceedings
    EditorsTu-Bao Ho, Hiroshi Motoda, Hiroshi Motoda, Ee-Peng Lim, Tru Cao, David Cheung, Zhi-Hua Zhou
    PublisherSpringer Verlag
    Pages525-537
    Number of pages13
    ISBN (Print)9783319180373
    DOIs
    Publication statusPublished - Jan 1 2015
    Event19th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2015 - Ho Chi Minh City, Viet Nam
    Duration: May 19 2015May 22 2015

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume9077
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Other

    Other19th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2015
    Country/TerritoryViet Nam
    CityHo Chi Minh City
    Period5/19/155/22/15

    Keywords

    • Chess
    • Classification
    • Data mining
    • Feature extraction
    • Prediction

    ASJC Scopus subject areas

    • Theoretical Computer Science
    • Computer Science(all)

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