The accuracy metric has been widely used for discriminating and selecting an optimal
solution in constructing an optimized classifier. However, the use of accuracy metric leads …

Abstract Receiver Operating Characteristic (ROC) curves are a standard way to display the
performance of a set of binary classifiers for all feasible ratios of the costs associated with …

This paper introduces a new method for learning algorithm evaluation and selection, with
empirical results based on classification. The empirical study has been conducted among 8 …

This work is builds on the study of the 10 top data mining algorithms identified by the IEEE
International Conference on Data Mining (ICDM) community in December 2006. We address …

In this chapter, the important concepts of bias and variance are introduced. After an intuitive
introduction to the bias/variance tradeoff, we discuss the bias/variance decompositions of …

A great many tools have been developed for supervised classification, ranging from early
methods such as linear discriminant analysis through to modern developments such as …

Machine learning techniques are being adopted for a variety of applications. With an
increase in the adoption of machine learning techniques, it is very important for the …

In the past several years, the field of machine learning has seen an explosion of interest and
excitement, with hundreds or thousands of algorithms developed for different tasks every …

This paper proposes an alternative to ROC representation, in which the expected cost of a
classifier is represented explicitly. This expected cost representation maintains many of the …

Optimal performance is desired for decision-making in any field with binary classifiers and
diagnostic tests, however common performance measures lack depth in information. The …