Learning from an Incomplete Information System with Continuous-Valued Attributes by a Rough Set Technique

2006 ◽  
pp. 568-577 ◽  
Author(s):  
Eric C. C. Tsang ◽  
Suyun Zhao ◽  
Daniel S. Yeung ◽  
John W. T. Lee
Keyword(s):  
Information System ◽  
Incomplete Information ◽  
Rough Set ◽  
Incomplete Information System

scholarly journals Extended Tolerance Relation to Define a New Rough Set Model in Incomplete Information Systems

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Do Van Nguyen ◽  
Koichi Yamada ◽  
Muneyuki Unehara
Keyword(s):  
Information System ◽  
Information Systems ◽  
Incomplete Information ◽  
Rough Set ◽  
Incomplete Information System ◽  
Tolerance Relation ◽  
The Core ◽  
Mathematical Properties ◽  
Incomplete Information Systems

This paper discusses and proposes a rough set model for an incomplete information system, which defines an extended tolerance relation using frequency of attribute values in such a system. It first discusses some rough set extensions in incomplete information systems. Next, “probability of matching” is defined from data in information systems and then measures the degree of tolerance. Consequently, a rough set model is developed using a tolerance relation defined with a threshold. The paper discusses the mathematical properties of the newly developed rough set model and also introduces a method to derive reducts and the core.

Generalized neighborhood systems-based pessimistic rough sets and their applications in incomplete information systems

2021 ◽  
pp. 1-13
Author(s):  
Jing Pang ◽  
Bingxue Yao ◽  
Lingqiang Li
Keyword(s):  
Information System ◽  
Information Systems ◽  
Incomplete Information ◽  
Rough Set ◽  
Rough Sets ◽  
Decision Model ◽  
Incomplete Information System ◽  
Basic Properties ◽  
Model Based ◽  
Incomplete Information Systems

In this paper, we point out that Lin’s general neighborhood systems-based rough set model is an extension of Qian’s optimistic rough set model, and thus called optimistic general neighborhood systmes-based rough set model. Then we present a new rough set model based on general neighborhood systems, and prove that it is an extension of Qian’s pessimistic rough set model. Later, we study the basic properties of the proposed pessimistic rough sets, and define the serial, reflexive, symmetric, transitive and Euclidean conditions for general neighborhood systems, and explore the further properties of related rough sets. Furthermore, we apply the pessimistic general neighborhood systems-based rough set model in the research of incomplete information system, and build a three-way decision model based on it. A simple practical example to show the effectiveness of our model is also presented.

scholarly journals Neighborhood Hypergraph Based Classification Algorithm for Incomplete Information System

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Feng Hu ◽  
Jin Shi
Keyword(s):  
Machine Learning ◽  
Information System ◽  
Incomplete Information ◽  
Rough Set ◽  
Missing Values ◽  
Rough Set Theory ◽  
Classification Algorithm ◽  
Data Sets ◽  
Incomplete Information System ◽  
Hypergraph Model

The problem of classification in incomplete information system is a hot issue in intelligent information processing. Hypergraph is a new intelligent method for machine learning. However, it is hard to process the incomplete information system by the traditional hypergraph, which is due to two reasons: (1) the hyperedges are generated randomly in traditional hypergraph model; (2) the existing methods are unsuitable to deal with incomplete information system, for the sake of missing values in incomplete information system. In this paper, we propose a novel classification algorithm for incomplete information system based on hypergraph model and rough set theory. Firstly, we initialize the hypergraph. Second, we classify the training set by neighborhood hypergraph. Third, under the guidance of rough set, we replace the poor hyperedges. After that, we can obtain a good classifier. The proposed approach is tested on 15 data sets from UCI machine learning repository. Furthermore, it is compared with some existing methods, such as C4.5, SVM, NavieBayes, andKNN. The experimental results show that the proposed algorithm has better performance via Precision, Recall, AUC, andF-measure.

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