Data imbalance is frequently encountered in biomedical applications. Resampling
techniques can be used in binary classification to tackle this issue. However such solutions …

Multi-label classification problems usually occur in tasks related to information retrieval, like
text and image annotation, and are receiving increasing attention from the machine learning …

Precision-Recall analysis abounds in applications of binary classification where true
negatives do not add value and hence should not affect assessment of the classifier's …

This paper provides new insight into maximizing F1 measures in the context of binary
classification and also in the context of multilabel classification. The harmonic mean of …

Decision tree optimization is notoriously difficult from a computational perspective but
essential for the field of interpretable machine learning. Despite efforts over the past 40 …

Multi-label classification deals with the problem where each instance is associated with
multiple class labels. Because evaluation in multi-label classification is more complicated …

Performance metrics for binary classification are designed to capture tradeoffs between four
fundamental population quantities: true positives, false positives, true negatives and false …

Modern retrieval systems are often driven by an underlying machine learning model. The
goal of such systems is to identify and possibly rank the few most relevant items for a given …

We carry out an in-depth investigation on a newly proposed Maximum F1-score Criterion
(MFC) discriminative training objective function for Goodness of Pronunciation (GOP) based …

Extreme multi-label classification (XMLC) is a problem of tagging an instance with a small
subset of relevant labels chosen from an extremely large pool of possible labels. Large label …