Deep learning (DL) is a branch of machine learning (ML) showing increasing promise in
medicine, to assist in data classification, novel disease phenoty** and complex decision …

Recently, deep learning (DL) methods have been introduced very successfully into human
activity recognition (HAR) scenarios in ubiquitous and wearable computing. Especially the …

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 …

There have been many attempts to classify imbalanced data, since this classification is
critical in a wide variety of applications related to the detection of anomalies, failures, and …

F-measures are popular performance metrics, particularly for tasks with imbalanced data
sets. Algorithms for learning to maximize F-measures follow two approaches: the empirical …

Feature selection is beneficial for improving the performance of general machine learning
tasks by extracting an informative subset from the high-dimensional features. Conventional …

We describe a method of incorporating taskspecific cost functions into standard conditional
log-likelihood (CLL) training of linear structured prediction models. Recently introduced in …

The F-measure, originally introduced in information retrieval, is nowadays routinely used as
a performance metric for problems such as binary classification, multi-label classification …

We study consistency properties of algorithms for non-decomposable performance
measures that cannot be expressed as a sum of losses on individual data points, such as …

Wood decomposition is a central process contributing to global carbon and nutrient cycling.
Quantifying the role of the major biotic agents of wood decomposition, ie insects and fungi, is …