Feature Extraction for Heroin-Use Classification Using Imbalanced Random Forest Methods

Author:

Beattie Matthew1ORCID,Nicholson Charles1ORCID

Affiliation:

1. Data Science and Analytics, University of Oklahoma, Norman, Oklahoma, USA

Publisher

Informa UK Limited

Subject

Psychiatry and Mental health,Public Health, Environmental and Occupational Health,Medicine (miscellaneous),Health (social science)

Reference27 articles.

1. Boyd, K., Costa, V. S., Davis, J. & Page, C. D. (2012). Unachievable region in precision-recall space and its effect on empirical evaluation [Paper presentation]. Proceedings of the 29th International Conference on Machine Learning, ICML 2012, 1, 639–646.

2. Center for Behavioral Health Statistics and Quality (2018). 2016 National Survey on Drug Use and Health Public Use File Codebook.

3. Nonmedical Prescription Opioid Use in Childhood and Early Adolescence Predicts Transitions to Heroin Use in Young Adulthood: A National Study

4. Chen, C., Liaw, A. & Breiman, L. (2004). Using random forest to learn imbalanced data. Department of Statistics, University of California, Berkeley, CA. Technical Report 666.

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