This textbook explores the different aspects of data mining from the fundamentals to the
complex data types and their applications, capturing the wide diversity of problem domains …

This paper addresses the repeated acquisition of labels for data items when the labeling is
imperfect. We examine the improvement (or lack thereof) in data quality via repeated …

In all these cases, labels can be obtained, but only at a significant cost to the end user. An
important observation is that all records are not equally important from the perspective of …

Many scalable data mining tasks rely on active learning to provide the most useful
accurately labeled instances. However, what if there are multiple labeling sources ('oracles' …

This paper addresses the repeated acquisition of labels for data items when the labeling is
imperfect. We examine the improvement (or lack thereof) in data quality via repeated …

In domains ranging from computer vision to natural language processing, machine learning
models have been shown to exhibit stark disparities, often performing worse for members of …

Most induction algorithms for building predictive models take as input training data in the
form of feature vectors. Acquiring the values of features may be costly, and simply acquiring …

In this paper, we address the ice-start problem, ie, the challenge of deploying machine
learning models when only a little or no training data is initially available, and acquiring …

Feature missing is a serious problem in many applications, which may lead to low quality of
training data and further significantly degrade the learning performance. While feature …

This chapter focuses on the interaction between active learning (AL) and class imbalance,
discussing (i) AL techniques designed specifically for dealing with imbalanced settings,(ii) …