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Activity Number: 187 - Contributed Poster Presentations: Korean International Statistical Society
Type: Contributed
Date/Time: Monday, July 30, 2018 : 10:30 AM to 12:20 PM
Sponsor: Korean International Statistical Society
Abstract #328701
Title: Total Signal Index: Measure of Noise Accumulation in Big Data
Author(s): Miriam Elman* and Jessica Minnier and Xiaohui Chang and Dongseok Choi
Companies: Oregon Health & Science Univ and Oregon Health & Science University and Oregon State University and Oregon Health & Science University
Keywords: Big data; Classification; Noise accumulation; total signal index
Abstract:

Noise accumulation can occur when there are many weak or unassociated predictors included in a model. Such noise can concentrate, obstructing true signal and the estimation of corresponding parameters. High dimensional data, settings in which the number of predictors is much larger than the sample size, are especially susceptible to noise accumulation. A common prediction problem in machine learning is classification, a type of supervised learning. In this presentation, we propose using Total Signal Index (TSI) to measure noise accumulation for classifications in high dimensional data. We present the theoretical computations of TSI for various scenarios with corresponding simulation results.


Authors who are presenting talks have a * after their name.

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