In score-level fusion the match scores output by multiple ranking algorithms are consolidated in order to render a decision about the class of an unlabeled entity. Typically, this consolidation method outputs a single scalar score which is subsequently used by the classification system.
The Semi-Supervised Learning (SSL) paradigm is an extension of supervised learning with the principle of inclusion of unlabeled in- stances, which are used as background knowledge. When using SSL for a classification task, the entire approach is usually denoted as a Semi-Supervised Classification (SSC) ( Chapelle et al., 2010 ), where unlabeled data can be…