TY - GEN
T1 - Optimizing support vector machine analysis in low density biological data sets
AU - Rivas, Pablo
AU - Moore, Sharon
AU - Iwaniec, Urszula
AU - Turner, Russell
AU - Grant, Kathy
AU - Baker, Erich
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/12
Y1 - 2018/12
N2 - We explore the effectiveness of Support Vector Machines (SVM) for classification in a sparse data set. Non-human primate models are utilized to analyze Alcohol Use Disorders (AUDs); however, the resulting data have a limited sample size. The challenge of low sample numbers and low replicates are explored using a variety of optimization strategies for feature extraction, including correlation, entropy, density, linear support vector machines for regression (SVR), backward SVR, and forward SVR. We investigate these approaches against the backdrop of the relationship between alcohol consumption and tibial bone mineral density. The results indicate that machine learning (ML) can effectively be used in cases of low and diverse biological data sets. The best relevance feature ranking strategies are correlation, SVR forward, and SVR backward.
AB - We explore the effectiveness of Support Vector Machines (SVM) for classification in a sparse data set. Non-human primate models are utilized to analyze Alcohol Use Disorders (AUDs); however, the resulting data have a limited sample size. The challenge of low sample numbers and low replicates are explored using a variety of optimization strategies for feature extraction, including correlation, entropy, density, linear support vector machines for regression (SVR), backward SVR, and forward SVR. We investigate these approaches against the backdrop of the relationship between alcohol consumption and tibial bone mineral density. The results indicate that machine learning (ML) can effectively be used in cases of low and diverse biological data sets. The best relevance feature ranking strategies are correlation, SVR forward, and SVR backward.
KW - Bone modeling
KW - Machine learning
KW - Relevance feature mapping
KW - Support vector regression
UR - http://www.scopus.com/inward/record.url?scp=85078516754&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85078516754&partnerID=8YFLogxK
U2 - 10.1109/CSCI46756.2018.00263
DO - 10.1109/CSCI46756.2018.00263
M3 - Conference contribution
AN - SCOPUS:85078516754
T3 - Proceedings - 2018 International Conference on Computational Science and Computational Intelligence, CSCI 2018
SP - 1357
EP - 1361
BT - Proceedings - 2018 International Conference on Computational Science and Computational Intelligence, CSCI 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 International Conference on Computational Science and Computational Intelligence, CSCI 2018
Y2 - 13 December 2018 through 15 December 2018
ER -