Sequence classification is essential for domains from medical diagnosis to online advertising. In these settings, data are typically proprietary, and annotations expensive acquire. Often times, so few available that training a robust model scratch impractical. Recently, knowledge amalgamation (KA) has emerged as promising strategy models without this hard-to-come-by labeled dataset. To achieve ...

Deep learning has been widely used in various tasks such as computer vision, natural language processing, predictive analysis, and recommendation systems the past decade. However, practical scenarios often lack labeled data, posing challenges for traditional supervised methods. Semi-supervised classification methods address this by leveraging both unlabeled data to enhance model performance, bu...

Multiple classifier systems have been originally proposed for supervised classification tasks. In the five editions of MCS workshop, most of the papers have dealt with design methods and applications of supervised multiple classifier systems. Recently, the use of multiple classifier systems has been extended to unsupervised classification tasks. Despite its practical relevance, semi-supervised ...