The key idea behind active learning is that a machine learning algorithm can achieve
greater accuracy with fewer labeled training instances if it is allowed to choose the training …

The era of behavioural big data has created new avenues for data science research, with
many new contributions stemming from academic researchers. Yet data controlled by …

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 …

The conformal predictions framework is a recent development in machine learning that can
associate a reliable measure of confidence with a prediction in any real-world pattern …

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 …

Feature selection helps reduce data acquisition costs in ML, but the standard approach is to
train models with static feature subsets. Here, we consider the dynamic feature selection …

Many real-life decision-making situations allow further relevant information to be acquired at
a specific cost, for example, in assessing the health status of a patient we may decide to take …

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 …

Different from previous imputation methods which impute missing values in the incomplete
samples by using the information in the complete samples, this paper proposes a Date-drive …