Most classification problems associate a single class to each example or instance. However,
there are many classification tasks where each instance can be associated with one or more …

Criteria for evaluating the performance of a classifier are an important part in its design. They
allow to estimate the behavior of the generated classifier on unseen data and can be also …

This paper presents a systematic analysis of twenty four performance measures used in the
complete spectrum of Machine Learning classification tasks, ie, binary, multi-class, multi …

In this survey we discuss the task of hierarchical classification. The literature about this field
is scattered across very different application domains and for that reason research in one …

Hierarchical multi-label classification (HMC) is a variant of classification where instances
may belong to multiple classes at the same time and these classes are organized in a …

Several natural language processing (NLP) tasks are defined as a classification problem in
its most complex form: Multi-label Hierarchical Extreme classification, in which items may be …

In this paper, we address the task of learning models for predicting structured outputs. We
consider both global and local predictions of structured outputs, the former based on a …

Multi-class pattern classification has many applications including text document
classification, speech recognition, object recognition, etc. Multi-class pattern classification …

Hierarchical classification addresses the problem of classifying items into a hierarchy of
classes. An important issue in hierarchical classification is the evaluation of different …

Background S. cerevisiae, A. thaliana and M. musculus are well-studied organisms in
biology and the sequencing of their genomes was completed many years ago. It is still a …