The F-measure is an important and commonly used performance metric for bi-nary prediction tasks. By combining precision and recall into a single score, itavoids disadvantages of simple metrics like the error rate, especially in cases ofimbalanced class distributions. The problem of optimizing the F-measure, thatis, of developing learning algorithms that perform optimally in the ...

Logistic regression is a widely used method in several fields. When applying logistic to imbalanced data, wherein the majority classes dominate minority classes, all class labels are estimated as “majority class.” In this study, we use an F-measure optimization improve performance of applied data. Although many methods adopt ratio estimators approximate F-measure, tends exhibit more bias than w...

For totally positive algebraic integers α 6= 0, 1 of degree d(α), we consider the set L of values of M(α) 1 d(α) = Ω(α), where M(α) is the Mahler measure of α. C. J. Smyth has found the four smallest values of L and conjectured that the fifth point is Ω((2 cos 2π 60 )2). We prove that this is so and, moreover, we give the sixth point of L.

The F-measure, originally introduced in information retrieval, is nowadays routinely used as a performance metric for problems such as binary classification, multi-label classification, and structured output prediction. Optimizing this measure remains a statistically and computationally challenging problem, since no closed-form maximizer exists. Current algorithms are approximate and typically ...