TY - GEN
T1 - Performance of supervised classifiers for damage scoring of zebrafish neuromasts
AU - Philip, Rohit C.
AU - Malladi, Sree Ramya S.P.
AU - Niihori, Maki
AU - Jacob, Abraham
AU - Rodriguez, Jeffrey J
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/9/21
Y1 - 2018/9/21
N2 - 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.
AB - 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.
KW - Neural network
KW - naive Bayes classifier
KW - random forest
KW - supervised learning
KW - support vector machine
UR - http://www.scopus.com/inward/record.url?scp=85055470130&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85055470130&partnerID=8YFLogxK
U2 - 10.1109/SSIAI.2018.8470377
DO - 10.1109/SSIAI.2018.8470377
M3 - Conference contribution
AN - SCOPUS:85055470130
T3 - Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation
SP - 113
EP - 116
BT - 2018 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2018
Y2 - 8 April 2018 through 10 April 2018
ER -