The drug development process is a major challenge in the pharmaceutical industry since it
takes a substantial amount of time and money to move through all the phases of developing …

Abstract ROC ('receiver operator characteristics') analysis is a visual as well as numerical
method used for assessing the performance of classification algorithms, such as those used …

The field of machine learning has matured to the point where many sophisticated learning
approaches can be applied to practical applications. Thus it is of critical importance that …

Receiver operating characteristics (ROC) graphs are useful for organizing classifiers and
visualizing their performance. ROC graphs are commonly used in medical decision making …

Rules–the clearest, most explored and best understood form of knowledge representation–
are particularly important for data mining, as they offer the best tradeoff between human and …

Statistical pattern recognition is a very active area of study andresearch, which has seen
many advances in recent years. New andemerging applications-such as data mining, web …

Receiver Operating Characteristics (ROC) graphs are a useful technique for organizing
classifiers and visualizing their performance. ROC graphs are commonly used in medical …

This paper provides an analysis of the behavior of separate-and-conquer or covering rule
learning algorithms by visualizing their evaluation metrics and their dynamics in coverage …

Neighborhood Covering (NC) is the union of homogeneous neighborhoods and provides a
set-level approximation of data distribution. Because of the nonparametric property and the …

We unify f-divergences, Bregman divergences, surrogate regret bounds, proper scoring
rules, cost curves, ROC-curves and statistical information. We do this by systematically …