a test data set st is accuracy a u2211 x u2208 st u2062 u2062 b u2062 l u2062 u2062 x l max 3 for traditional accuracy based problems a is always normalized by dividing isti for cost sensitive problems a is usually represented in some measure of benefits such as dollar amount for cost sensitive problems we sometimes use u201c total benefits u201d to mean accuracy the strawman algorithm is based on averaging ensemble 3 assume that a data set s is partitioned into k disjoint subsets s j with equal size a base level model c j is trained from each s j given an example x each classifier outputs individual expected benefit based on probability p j l i u2032 x e j u2061 l i u2758 x u2211 l i u2032 u2062 u2062 b u2062 l i u2032 l i u00b7 p j u2062 u2062 l i u2032 u2758 x 4 e k u2061 l i u2758 x u2211 1 k u2062 u2062 e j u2061 l i u2758 x k 5 we then predict the class label with the highest expected return as in eq 2 optimal decision l k arg max t i e k l i x 6 a clear advantage is that the strawman algorithm scans the dataset exactly once as compared to two scans by meta learning and multiple scans by bagging and boosting in previous research 3 the accuracy by the strawman algorithm is also significantly higher than both meta learning and bagging 3 explains the statistical reason why the averaging ensemble is also more likely to have.