Optimal training sets for Bayesian prediction of MeSH® assignment

Objectives: The aim of this study was to improve naive Bayes prediction of Medical Subject Headings (MeSH) assignment to documents using optimal training sets found by an active learning inspired method. Design: 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 (C-1 class) and those documents not including a given MeSH term (C-1 class) that are closest to the C-1 class. These small sets were used to predict MeSH assignments in the MEDLINE (R) database. Measurements: Average precision was used to compare MeSH assignment using the naive Bayes learner trained on the whole training set, optimal sets, and random sets. The authors compared 95% lower confidence limits of average precisions of naive 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, naive 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 naive 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 naive Bayes. Conclusion: Using a smaller optimal training set greatly improved learning with naive 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.