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Flying Insect Detection and Classification with Inexpensive Sensors
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Better-than-chance classification for signal detection.

Jonathan D Rosenblatt1, Yuval Benjamini2, Roee Gilron3

  • 1Department of IE&M and Zlotowsky Center for Neuroscience, Ben Gurion University of the Negev, P.O. 653, Beer Sheva, 84105 Israel.

Biostatistics (Oxford, England)
|October 16, 2019
PubMed
Summary

Using classifier accuracy to detect differences is often underpowered and computationally expensive. Alternative statistical tests offer higher detection probability and efficiency, especially in fields like neuroimaging and genetics.

Keywords:
High dimensionMultivariate testingNeuroimagingStatistical geneticsSupervised learning

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Area of Science:

  • Machine Learning
  • Statistical Analysis
  • Bioinformatics

Background:

  • Classifier accuracy is frequently used to test for significant differences, particularly in neuroimaging and genetics.
  • This approach, while common, treats accuracy as a test statistic for signal detection.

Purpose of the Study:

  • To evaluate the power and computational efficiency of accuracy-based statistics versus traditional statistical tests.
  • To identify reasons for the low power of accuracy-based tests and suggest improvements.

Main Methods:

  • Simulations were conducted to compare classification accuracy-based statistics with multivariate statistical tests.
  • The study examined factors contributing to the low power of accuracy-based tests, such as data discreteness and inefficient data usage.

Main Results:

  • Accuracy-based statistics demonstrated a lower probability of detecting differences between distributions compared to multivariate statistical tests.
  • Accuracy-based tests were found to be computationally more demanding than bona fide statistical tests.

Conclusions:

  • Using classifier accuracy as a primary test statistic for signal detection is an underpowered and inefficient strategy.
  • Improvements, such as replacing V-fold cross-validation with Leave-One-Out Bootstrap, can enhance classifier evaluation power.