With the wide adoption of building automation system, and the advancement of data,
sensing, and machine learning techniques, data-driven fault detection and diagnostics …

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 …

For better service and energy savings, improved fault detection and diagnosis (FDD) of
building energy systems is of great importance. To achieve this aim, ensemble learning is …

We present a theoretical analysis of F-measures for binary, multiclass and multilabel
classification. These performance measures are non-linear, but in many scenarios they are …

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

Cellular respiration provides direct energy substances for living organisms. Electron storage
and transportation should be completed through electron transport chains during the cellular …

Smart homes expects to improve the convenience, comfort, and energy efficiency of the
residents by connecting and controlling various appliances. As the personal information and …

Aiming at the minority samples cannot be effectively diagnosed when the samples are
limited and imbalanced, a multiple classifier ensemble of the weighted and balanced …

The ever-lasting challenge of detecting and mitigating failures in computer networks has
become more essential than ever; especially with the enormous number of smart devices …

Many multi-label classifiers provide a real-valued score for each class. A well known design
approach consists of tuning the corresponding decision thresholds by optimising the …