Corpus ID: 7315953

Learning to Predict Extremely Rare Events

@inproceedings{Weiss2000LearningTP,
  title={Learning to Predict Extremely Rare Events},
  author={G. Weiss and H. Hirsh},
  year={2000}
}
  • G. Weiss, H. Hirsh
  • Published 2000
  • Computer Science
  • This paper describes Timeweaver, a genetic-based machine learning system that predicts events by identifying temporal and sequential patterns in data. This paper then focuses on the issues related to predicting rare events and discusses how Timeweaver addresses these issues. In particular, we describe how the genetic algorithm’s fitness function is tailored to handle the prediction of rare events, by factoring in the precision and recall of each prediction rule. 
    Rare events and imbalanced datasets: an overview
    • 9
    Rare Class Mining: Progress and Prospect
    • 27
    • PDF
    A process for predicting manhole events in Manhattan
    • 53
    • PDF
    Data duplication: an imbalance problem ?
    • 57
    • PDF
    Individual-Level Fatality Prediction of COVID-19 Patients Using AI Methods
    Topic Time Series Analysis of Microblogs
    • 25
    • PDF
    Classifier evaluation under limited resources
    • 30
    • PDF
    Predicting relapse of schizophrenia
    • Petr NALEVKAa
    • 4
    • PDF

    References

    Publications referenced by this paper.
    SHOWING 1-4 OF 4 REFERENCES
    Learning to Predict Rare Events in Event Sequences
    • 270
    • PDF
    Timeweaver: a genetic algorithm for identifying predictive patterns in sequences of events
    • 81
    • PDF
    Discovering Patterns in Sequences of Events
    • 114
    Fast Effective Rule Induction
    • 3,839
    • Highly Influential
    • PDF