How do we choose the best model? The impact of cross-validation design on model evaluation for buried threat detection in ground penetrating radar

Jordan M. Malof, Daniël Reichman, Leslie M. Collins

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

A great deal of research has been focused on the development of computer algorithms for buried threat detection (BTD) in ground penetrating radar (GPR) data. Most recently proposed BTD algorithms are supervised, and therefore they employ machine learning models that infer their parameters using training data. Cross-validation (CV) is a popular method for evaluating the performance of such algorithms, in which the available data is systematically split into N disjoint subsets, and an algorithm is repeatedly trained on N-1 subsets and tested on the excluded subset. There are several common types of CV in BTD, which vary principally upon the spatial criterion used to partition the data: site-based, lane-based, region-based, etc. The performance metrics obtained via CV are often used to suggest the superiority of one model over others, however, most studies utilize just one type of CV, and the impact of this choice is unclear. Here we employ several types of CV to evaluate algorithms from a recent large-scale BTD study. The results indicate that the rank-order of the performance of the algorithms varies substantially depending upon which type of CV is used. For example, the rank-1 algorithm for region-based CV is the lowest ranked algorithm for site-based CV. This suggests that any algorithm results should be interpreted carefully with respect to the type of CV employed. We discuss some potential interpretations of performance, given a particular type of CV.

Original languageEnglish
Title of host publicationDetection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIII
EditorsSteven S. Bishop, Jason C. Isaacs
PublisherSPIE
ISBN (Electronic)9781510617674
DOIs
StatePublished - 2018
EventDetection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIII 2018 - Orlando, United States
Duration: Apr 16 2018Apr 18 2018

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume10628
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceDetection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIII 2018
Country/TerritoryUnited States
CityOrlando
Period04/16/1804/18/18

Keywords

  • buried threat detection
  • cross-validation
  • ground penetrating radar
  • model evaluation
  • supervised learning

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