Update of approximations in ordered information systems under variations of attribute and object set

Abstract

Many collected data from real world applications often evolve when new attributes or objects are inserted or old ones are removed. The set approximations of ordered information systems (OIS) need to be updated from time to time for further data reduction, analysis, or decision-making. Incremental approaches are feasible and efficient techniques for updating approaches when any variation occurs. In this paper, considering OIS for multi-criteria classification problems, we discuss the principles of incrementally updating approximations in dominance relation based method in four different types of dynamic environments which combine the changes of both attribute set and object set. In each dynamic environment, the corresponding updating principles and algorithm are given with detail proofs. The experimental results and analysis on UCI data sets show that the proposed incremental approach outperforms the non-incremental method and the integration of current incremental algorithms in the implementation efficiency.