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    Geometric Heuristics for Transfer Learning in Decision Trees

    Chaubal, Siddhesh and Rzepecki, Mateusz and Nicholson, Patrick K. and Piao, Guangyuan and Sala, Alessandra (2021) Geometric Heuristics for Transfer Learning in Decision Trees. International Conference on Information and Knowledge Management, Proceedings. pp. 151-160.

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    Motivated by a network fault detection problem, we study how recall can be boosted in a decision tree classifier, without sacrificing too much precision. This problem is relevant and novel in the context of transfer learning (TL), in which few target domain training samples are available. We define a geometric optimization problem for boosting the recall of a decision tree classifier, and show it is NP-hard. To solve it efficiently, we propose several near-linear time heuristics, and experimentally validate these heuristics in the context of TL. Our evaluation includes 7 public datasets, as well as 6 network fault datasets, and we compare our heuristics with several existing TL algorithms, as well as exact mixed integer linear programming (MILP) solutions to our optimization problem. We find that our heuristics boost recall in a manner similar to optimal MILP solutions, yet require several orders of magnitude less compute time. In many cases the

    Item Type: Article
    Keywords: Transfer learning; Decision trees; Random forests; Classification;
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 15630
    Identification Number:
    Depositing User: Guangyuan Piao
    Date Deposited: 08 Mar 2022 11:21
    Journal or Publication Title: International Conference on Information and Knowledge Management, Proceedings
    Publisher: ACM
    Refereed: Yes
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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