Transaction Anomaly Detection CMPS 242 - Project Report
نویسنده
چکیده
This project uses a series of learning approaches for a transaction anomaly detection problem. The input data is a 19-feature dataset of transaction records with high binary class skewness (1% vs 99%). We apply Logistic Regression, AdaBoost and Boosting Trees and we use Lift and prediction accuracy metrics to evaluate our results. All approaches were based on the material covered in the course notes [5] of this class.
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تاریخ انتشار 2009