Attribute Reduction on Distributed Incomplete Decision Information System
نویسندگان
چکیده
Attribute reduction is an important issue in rough set theory. This paper mainly studies attribute reduction of distributed incomplete decision information system (DIDIS). Firstly, the definition of rough set in DIDIS is developed. Next, an algorithm for attribute reduction of DIDIS is proposed. In the end, two groups of experiments are conducted to prove the effectiveness of the proposed method. The results show that our method can remove redundant attributes of DIDIS, and does not reduce the classification capability of the system. In addition, the results indicate that the change of data missing rate has weak effect on attribute reduction with the similarity relation, but strong effect on attribute reduction with the tolerance relation.
منابع مشابه
Variable Rough Set Model and Its Knowledge Reduction for Incomplete and Fuzzy Decision Information Systems
The information systems with incomplete attribute values and fuzzy decisions commonly exist in practical problems. On the base of the notion of variable precision rough set model for incomplete information system and the rough set model for incomplete and fuzzy decision information system, the variable rough set model for incomplete and fuzzy decision information system is constructed, which is...
متن کاملA New Extended Analytical Hierarchy Process Technique with Incomplete Interval-valued Information for Risk Assessment in IT Outsourcing
Information technology (IT) outsourcing has been recognized as a new methodology in many organizations. Yet making an appropriate decision with regard to selection and use of these methodologies may impose uncertainties and risks. Estimating the occurrence probability of risks and their impacts organizations goals may reduce their threats. In this study, an extended analytical hierarchical proc...
متن کاملA Distance-based Method for Attribute Reduction in Incomplete Decision Systems
There are limitations in recent research undertaken on attribute reduction in incomplete decision systems. In this paper, we propose a distance-based method for attribute reduction in an incomplete decision system. In addition, we prove theoretically that our method is more effective than some other methods.
متن کاملGeneralized Discernibility Function Based Attribute Reduction in Incomplete Decision Systems
A rough set approach for attribute reduction is an important research subject in data mining and machine learning. However, most attribute reduction methods are performed on a complete decision system table. In this paper, we propose methods for attribute reduction in static incomplete decision systems and dynamic incomplete decision systems with dynamically-increasing and decreasing conditiona...
متن کاملRough Set Approach to Incomplete Information Systems
In the paper we present Rough Set approach to reasoning in incomplete information systems. We propose reduction of knowledge that eliminates only that information, which is not essential from the point of view of classification or decision making. In our approach we make only one assumption about unknown values: the real value of a missing attribute is one from the attribute domain. However, we...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2017