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Code and Dataset for TARP Detection Benchmarks

By Kelsie Larson1, Mireille Boutin1

Purdue University

The TARP method uses random projections, followed by threshold classifications, to construct receiver-operating characteristic curves and uncover underlying structure in the given data.

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Version 1.0 - published on 16 May 2017 doi:10.4231/R7ST7MVC - cite this Content may change until committed to the archive on 16 Jun 2017

Licensed under CC0 1.0 Universal

AAC_CT_benchmark_shade.png

Description

This is the code for constructing the Thresholding After Random Projection (TARP) benchmark for a detection problem using labeled data. The benchmark is a curve in a two-dimensional plane whose x-axis is the Area Above the ROC Curve (AAC) and whose y-axis is (either training or testing) computational cost (CC). We build the curve by first estimating the AAC and CC for a sequence of TARP-based detection methods with increasing complexity. Each TARP-based detection method defines a point in the plane given by the AAC and the CC of that method. The curve is obtained by interpolating the sequence of points corresponding to the sequence of TARP method performance (AAC-CC) with a straight line. The code herein produces a CSV file containing the sequence of points use to build this benchmark curves.

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