Estimating class sizes by adjusting fallible classifier results

Estimating Class Membership Probabilities using Classifier Learners

Absent data generating classifier for imbalanced class sizes

We propose an algorithm for two-class classification problems when the training data are imbalanced. This means the number of training instances in one of the classes is so low that the conventional classification algorithms become ineffective in detecting the minority class. We present a modification of the kernel Fisher discriminant analysis such that the imbalanced nature of the problem is e...

Scorecard construction with unbalanced class sizes

A long-running issue in scorecard construction in retail banking is how to handle dramatically unbalanced class sizes. This is important because, in many applications, the class sizes are very different. We describe the impact ignoring such imbalance can have and review the various strategies which have been proposed for tackling it, embedding them in a common theoretical framework. We then des...

Estimating Commit Sizes Efficiently

The quantitative analysis of software projects can provide insights that let us better understand open source and other software development projects. An important variable used in the analysis of software projects is the amount of work being contributed, the commit size. Unfortunately, post-facto, the commit size can only be estimated, not measured. This paper presents several algorithms for e...

Estimating Answer Sizes for XML Queries

Estimating the sizes of query results, and intermediate results, is crucial to many aspects of query processing. In particular, it is necessary for effective query optimization. Even at the user level, predictions of the total result size can be valuable in “next-step” decisions, such as query refinement. This paper proposes a technique to obtain query result size estimates effectively in an XM...