Rough Set Extension under Incomplete Information System with “?” Values

Authors

DOI:

https://doi.org/10.18063/scr.v2i1.385

Keywords:

Incomplete information system, Non-symmetric similarity relation based rough set, Maximal consistent block relation, Limited tolerance relation

Abstract

Classical rough set theory (RST) can't process incomplete information system (IIS) because it is based on an indiscernibility relation which is a kind of equivalent relation. In the literature a non-symmetric similarity relation based rough set model (NS-RSM) has been introduced as an extended model under IIS with ``?" values directly. Unfortunately, in this model objects in the same similarity class are not necessarily similar to each other and may belong to different target classes. In this paper, a new inequivalent relation called Maximal Limited Consistent block relation (MLC) is proposed. The proposed MLC relation improves the lower approximation accuracy by finding the maximal limited blocks of indiscernible objects in IIS with ``?" values. Maximal Limited Similarity rough set model (MLS) is introduced as an integration between our proposed relation (MLC) and NS-RSM. The resulted MLS model works efficiently under IIS with ``?" values. Finally, an illustrative example is given to validate MLS model. Furthermore, approximation accuracy comparisons have been conducted among NS-RSM and MLS. The results of this work demonstrate that the MLS model outperform NS-RSM.

Author Biography

Ahmed Hamed Hussein, soft computing; classification, optimization; rough sets; clustering; machine learning

I am a Lectuerer Assistant at Faculty of Computers and In formatics - Suez Canal University

My research is in algorithmic machine learning, and spans modeling, optimization algorithms, theory and applications. In particular, we have been working on exploiting mathematical structure for discrete and combinatorial machine learning problems, for robustness and for scaling machine learning algorithms.

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Published

2018-08-01

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Section

Original Research Articles