Classifiers can provide counts of items per class, but systematic classification errors yield
biases (eg, if a class is often misclassified as another, its size may be under-estimated). To …

A fundamental tenet assumed by many classification algorithms is the presumption that both
training and testing samples are drawn from the same distribution of data-this is the …

From personalized ad delivery and healthcare to criminal sentencing, more decisions are
made with help from methods developed in the fields of data mining and machine learning …

Conventional methods for classifier design often suffer from having two conflicting goals-to
develop arbitrarily complex decision boundaries to suit a given problem, and at the same …

The prevailing approach to evaluating classifiers in the machine learning community
involves comparing the performance of several algorithms over a series of usually unrelated …

We investigate the factors leading to producing suboptimal models when training and test
class distributions (or misclassification costs) are matched. Our result shows that model …

There are several performance metrics that have been proposed for evaluating a
classification model, eg, accuracy, error rates, precision, recall, etc. While it is known that …

We propose a preprocessing step to classification that applies a clustering algorithm to the
training set to discover local patterns in the attribute or input space. We demonstrate how …

We develop a solution framework for mitigating algorithmic bias in machine-learning
classifiers. We consider an interactive problem setting where Alice (eg, the firm) proposes to …

Recent years have witnessed an upsurge of interest in lossy compression due to its potential
to significantly reduce data volume with adequate exploitation of the spatiotemporal …