Concept Learning from Triadic Data

We propose extensions of the classical JSM-method and the Naı̈ve Bayesian classifier for the case of triadic relational data. We performed a series of experiments on various types of data (both real and synthetic) to estimate quality of classification techniques and compare them with other classification algorithms that generate hypotheses, e.g. ID3 and Random Forest. In addition to classification precision and recall we also evaluated the time performance of the proposed methods. c © 2014 The Authors. Published by Elsevier B.V. Selection and peer-review under responsibility of the Organizing Committee of ITQM 2014.