Performance of SVM and Bayesian classifiers on the systematic review classification task

摘要

In the July 2010 issue of JAMIA, Matwin et al published an article entitled 'A new algorithm for reducing the workload of experts in performing systematic reviews.'1 Briefly, the work proposes a factorized variant of the complement Naive Bayes classifier as an improvement, using weight engineering on the features (FCNB/WE). The prior work of Cohen et al in this area is cited, and the data set made public along with this prior work is used for the evaluation.The Matwin et al article corftpares the authors' proposed system against the early Cohen et al published voting perceptron (VP) classifier results, using the 'work saved over sampling at 95% recall' (WSS@95) measure proposed in that paper. However, the article notes that WSS@95 figures were not published in Cohen's later work based on the support vector machine (SVM) classifier,3 4 and states that these figures were not available for comparison.