Optimal training sets for Bayesian prediction of MeSH assignment.
OBJECTIVE
The aim of this study was to improve naïve Bayes prediction of Medical Subject Headings (MeSH) assignment to documents using optimal training sets found by an active learning inspired method.


METHODS
The authors selected 20 MeSH terms whose occurrences cover a range of frequencies. For each MeSH term, they found an optimal training set, a subset of the whole training set. An optimal training set consists of all documents including a given MeSH term (C1 class) and those documents not including a given MeSH term (C(-1) class) that are closest to the C1 class. These small sets were used to predict MeSH assignments in the MEDLINE database.


METHODS
Average precision was used to compare MeSH assignment using the naïve Bayes learner trained on the whole training set, optimal sets, and random sets. The authors compared 95% lower confidence limits of average precisions of naïve Bayes with upper bounds for average precisions of a K-nearest neighbor (KNN) classifier.


RESULTS
For all 20 MeSH assignments, the optimal training sets produced nearly 200% improvement over use of the whole training sets. In 17 of those MeSH assignments, naïve Bayes using optimal training sets was statistically better than a KNN. In 15 of those, optimal training sets performed better than optimized feature selection. Overall naïve Bayes averaged 14% better than a KNN for all 20 MeSH assignments. Using these optimal sets with another classifier, C-modified least squares (CMLS), produced an additional 6% improvement over naïve Bayes.


CONCLUSIONS
Using a smaller optimal training set greatly improved learning with naïve Bayes. The performance is superior to a KNN. The small training set can be used with other sophisticated learning methods, such as CMLS, where using the whole training set would not be feasible.
Sohn
Sunghwan
S
National Center for Biotechnology Information, National Library of Medicine, National Institute of Health, Bethesda, MD 20894, USA. sohn@ncbi.nlm.nih.gov
Kim
Won
W
Comeau
Donald C
DC
Wilbur
W John
WJ
D000043:Abstracting and Indexing / Q000379:methods*; D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D046650:Medical Subject Headings; D009323:Natural Language Processing