A decision tree approach to predicting recidivism in domestic violence

Senuri Wijenayake, Timothy Graham, Peter Christen*

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    12 Citations (Scopus)

    Abstract

    Domestic violence (DV) is a global social and public health issue that is highly gendered. Being able to accurately predict DV recidivism, i.e., re-offending of a previously convicted offender, can speed up and improve risk assessment procedures for police and front-line agencies, better protect victims of DV, and potentially prevent future re-occurrences of DV. Previous work in DV recidivism has employed different classification techniques, including decision tree (DT) induction and logistic regression, where the main focus was on achieving high prediction accuracy. As a result, even the diagrams of trained DTs were often too difficult to interpret due to their size and complexity, making decision-making challenging. Given there is often a trade-off between model accuracy and interpretability, in this work our aim is to employ DT induction to obtain both interpretable trees as well as high prediction accuracy. Specifically, we implement and evaluate different approaches to deal with class imbalance as well as feature selection. Compared to previous work in DV recidivism prediction that employed logistic regression, our approach can achieve comparable area under the ROC curve results by using only 3 of 11 available features and generating understandable decision trees that contain only 4 leaf nodes.

    Original languageEnglish
    Title of host publicationTrends and Applications in Knowledge Discovery and Data Mining - PAKDD 2018 Workshops, BDASC, BDM, ML4Cyber, PAISI, DaMEMO, Revised Selected Papers
    EditorsMohadeseh Ganji, Lida Rashidi, Benjamin C.M. Fung, Can Wang
    PublisherSpringer Verlag
    Pages3-15
    Number of pages13
    ISBN (Print)9783030045029
    DOIs
    Publication statusPublished - 2018
    Event22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2018 - Melbourne, Australia
    Duration: 3 Jun 20183 Jun 2018

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume11154 LNAI
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2018
    Country/TerritoryAustralia
    CityMelbourne
    Period3/06/183/06/18

    Fingerprint

    Dive into the research topics of 'A decision tree approach to predicting recidivism in domestic violence'. Together they form a unique fingerprint.

    Cite this