Context-dependent incremental learning of good maximally redundant tests
Xenia Aleksandrovna Naidenova, Vladimir Andreevich Parkhomenko, Konstantin Vladimirovich Shvetsov · 2015
A new approach to incremental learning of Good Maximally Redundant Diagnostic Tests (GMRTs) is advanced. A GMRT is a special formal concept in Formal Concept Analysis. Mining GMRTs from data is based on Galois' lattice construction. Four situations of learning are considered: inserting an object (value) and deleting an object (value). The approach proposed can be very useful for many information retrieval applications related to the changeable environment: mining logical rules from dynamic databases, intrusion detection, Web page classification, Web mining, constructing dynamic knowledge bases and many others.