Transductive Learning from Relational Data

Transduction is an inference mechanism “from particular to particular”. Its application to classification tasks implies the use of both labeled (training) data and unlabeled (working) data to build a classifier whose main goal is that of classifying (only) unlabeled data as accurately as possible. Unlike the classical inductive setting, no general rule valid for all possible instances is generated. Transductive learning is most suited for those applications where the examples for which a prediction is needed are already known when training the classifier. Several approaches have been proposed in the literature on building transductive classifiers from data stored in a single table of a relational database. Nonetheless, no attention has been paid to the application of the transduction principle in a (multi-)relational setting, where data are stored in multiple tables of a relational database. In this paper we propose a new transductive classifier, named TRANSC, which is based o...

• Type: Conference
• Year: 2007
• Where: MLDM
• Authors: Michelangelo Ceci, Annalisa Appice, Nicola Barile, Donato Malerba
• Keywords: Building Transductive Classifiers, Machine Learning, MLDM 2007, Transductive Classifier, Transductive Learning