Learning In-between Concept Descriptions Using Iterative Induction

摘要

Post and prior to learning concept perception may vary. Inductive learning systems support learning according to concepts provided and miss to identify concepts, which are hidden or implied by training data sequences. A training instance, known to belong to concept 'A' either participates in the formation of rule about concept 'A' or indicates a problematic instance. A test instance known to belong to concept 'A' is either classified correctly or misclassi-fied. Yet an instance (either training or test) may be pointing to a blurred description of concept A and thus may lie in between two (or more) concepts. This paper presents a synergistic iterative process model, SIR, which supports the resolution of conflict or multi-class assignment of instances during inductive learning. The methodology is based on two steps iteration: (a) induction and (b) formation of new concepts. Experiments on real-world domains from medicine, genomics and finance are presented and discussed.