Efficient big data model selection with applications to fraud detection

Gregory Vaughan

International Journal of Forecasting, 2020, vol. 36, issue 3, 1116-1127

Abstract: As the volume and complexity of data continues to grow, more attention is being focused on solving so-called big data problems. One field where this focus is pertinent is credit card fraud detection. Model selection approaches can identify key predictors for preventing fraud. Stagewise Selection is a classic model selection technique that has experienced a revitalized interest due to its computational simplicity and flexibility. Over a sequence of simple learning steps, stagewise techniques build a sequence of candidate models that is less greedy than the stepwise approach.

Keywords: Big data; Stagewise estimation; Sub-sampling; Fraud detection; Clustered data (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:36:y:2020:i:3:p:1116-1127

DOI: 10.1016/j.ijforecast.2018.03.002

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