Effective monotone knowledge integration in kernel support vector machines

C. Bartley, Wei Liu, Mark Reynolds

    Research output: Chapter in Book/Conference paperConference paperpeer-review

    4 Citations (Scopus)


    © Springer International Publishing AG 2016.In many machine learning applications there exists prior knowledge that the response variable should be increasing (or decreasing) in one or more of the features. This is the knowledge of ‘monotone’ relationships. This paper presents two new techniques for incorporating monotone knowledge into non-linear kernel support vector machine classifiers. Incorporating monotone knowledge is useful because it can improve predictive performance, and satisfy user requirements. While this is relatively straight forward for linear margin classifiers, for kernel SVM it is more challenging to achieve efficiently. We apply the new techniques to real datasets and investigate the impact of monotonicity and sample size on predictive accuracy. The results show that the proposed techniques can significantly improve accuracy when the unconstrained model is not already fully monotone, which often occurs at smaller sample sizes. In contrast, existing techniques demonstrate a significantly lower capacity to increase monotonicity or achieve the resulting accuracy improvements.
    Original languageEnglish
    Title of host publication Advanced Data Mining and Applications. ADMA 2016. Lecture Notes in Computer Science
    EditorsJianxin Li, Xue Li, Shuliang Wang, Jinyan Li, Quan Z. Sheng
    Number of pages16
    ISBN (Print)9783319495859
    Publication statusPublished - 2016
    Event12th International Conference on Advanced Data Mining and Applications - Gold Coast, Australia
    Duration: 12 Dec 201615 Dec 2016

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume10086 LNAI
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349


    Conference12th International Conference on Advanced Data Mining and Applications
    CityGold Coast


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