document: Threshold selection for web-page classification with highly skewed class distribution We propose a novel cost-efficient approach to threshold selection for binary web-page classification problems with imbalanced class distributions . In many binary-classification tasks the distribution of classes is highly skewed . In such problems , using uniform random sampling in constructing sample sets for threshold setting requires large sample sizes in order to include a statistically sufficient number of examples of the minority class . On the other hand , manually labeling examples is expensive and budgetary considerations require that the size of sample sets be limited . These conflicting requirements make threshold selection a challenging problem . Our method of sample-set construction is a novel approach based on stratified sampling , in which manually labeled examples are expanded to reflect the true class distribution of the web-page population . Our experimental results show that using false positive rate as the criterion for threshold setting results in lower-variance threshold estimates than using other widely used accuracy measures such as F1 and precision .
doc_bio_tags: B I O O O O O B I I O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O