Performance of supervised classifiers for damage scoring of zebrafish neuromasts

Rohit C. Philip, Sree Ramya S.P. Malladi, Maki Niihori, Abraham Jacob, Jeffrey J Rodriguez

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Supervised machine learning schemes are widely used to perform classification tasks. There is a wide variety of classifiers in use today, such as single- and multi-class support vector machines, k-nearest neighbors, decision trees, random forests, naive Bayes classifiers with or without kernel density estimation, linear discriminant analysis, quadratic discriminant analysis, and numerous neural network architectures. Our prior work used high-level shape, intensity, and texture features as predictors in a single-class support vector machine classifier to classify images of zebrafish neuromasts obtained using confocal microscopy into four discrete damage classes. Here, we analyze the performance of a multitude of supervised classifiers in terms of mean absolute error using these high-level features as predictors. In addition, we also analyze performance while using raw pixel data as predictors.

Original languageEnglish (US)
Title of host publication2018 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages113-116
Number of pages4
Volume2018-April
ISBN (Electronic)9781538665688
DOIs
StatePublished - Sep 21 2018
Event2018 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2018 - Las Vegas, United States
Duration: Apr 8 2018Apr 10 2018

Other

Other2018 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2018
CountryUnited States
CityLas Vegas
Period4/8/184/10/18

Keywords

  • naive Bayes classifier
  • Neural network
  • random forest
  • supervised learning
  • support vector machine

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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