# Incomplete Information Tables and Rough Classification

@article{Stefanowski2001IncompleteIT, title={Incomplete Information Tables and Rough Classification}, author={Jerzy Stefanowski and Alexis Tsouki{\'a}s}, journal={Computational Intelligence}, year={2001}, volume={17} }

The rough set theory, based on the original definition of the indiscernibility relation, is not useful for analysing incomplete information tables where some values of attributes are unknown. In this paper we distinguish two different semantics for incomplete information: the “missing value” semantics and the “absent value” semantics. The already known approaches, e.g. based on the tolerance relations, deal with the missing value case. We introduce two generalisations of the rough sets theory…

## 348 Citations

### On modeling similarity and three-way decision under incomplete information in rough set theory

- Computer ScienceKnowl. Based Syst.
- 2020

### A Modified Rough Set Approach to Incomplete Information Systems

- Computer ScienceAdv. Decis. Sci.
- 2007

This paper introduces the modified similarity relation denoted by MSIM that is dependent on the number of missing values with respect to theNumber of the whole defined attributes for each object.

### Rough Sets and Rule Induction in Imperfect Information Systems

- Computer Science
- 2014

This paper first discusses probabilities of attribute values assuming different type of attributes in real applications, and proposes a generalized method of probability of matching, which is then used to define valued tolerance/similarity relations and to develop new rough set models based on the valued tolerance and similarity relations.

### Dominance-based rough set approach to incomplete interval-valued information system

- Computer ScienceData Knowl. Eng.
- 2009

### A Relative Tolerance Relation of Rough Set for Incomplete Information Systems

- Computer ScienceSCDM
- 2018

This work proposes a relative tolerance relation of rough set (RTRS) to handling incomplete information systems, which it has flexibility and precisely for data classification in incomplete Information systems.

### Rough Set Approach to Incomplete Data

- Computer ScienceICAISC
- 2004

Two main cases of missing attribute values are discussed: lost values and “do not care” conditions (the original values were irrelevant).

### A new limited tolerance relation for attribute selection in incomplete information systems

- Computer Science2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)
- 2015

The novelty of the approach is that, unlike previous approach that use limited tolerance relation, it takes into consideration the similarity precision between objects in incomplete information systems and therefore this is the first work that used similarity precision.

### On Probability of Matching in Probability Based Rough Set Definitions

- Computer Science2013 IEEE International Conference on Systems, Man, and Cybernetics
- 2013

The paper first discusses probabilities of attribute values assuming different type of attributes in real applications, and proposes a generalized method of probability of matching, which could be used for defining valued tolerance/similarity relations in rough set approaches.

### On Probability of Matching in Probability Based Rough Set Definitions

- Computer Science2013 IEEE International Conference on Systems, Man, and Cybernetics
- 2013

The paper first discusses probabilities of attribute values assuming different type of attributes in real applications, and proposes a generalized method of probability of matching, which could be used for defining valued tolerance/similarity relations in rough set approaches.

### Rough Sets Based on Possibly Indiscernible Classes in Incomplete Information Tables with Continuous Values

- Computer ScienceAISI
- 2019

The approach based on possible world semantics gives the same approximations as ones obtained from the extended approach, which is proposed in the previous work using indiscernible classes, and justifies the approach developed in this paper.

## References

SHOWING 1-10 OF 25 REFERENCES

### Handling Missing Values in Rough Set Analysis of Multi-Attribute and Multi-Criteria Decision Problems

- Computer ScienceRSFDGrC
- 1999

An extension of the rough set methodology based on indiscernibility relations to deal with multi-criteria sorting problems when the data table is often not complete because some data are missing.

### Rough Set Processing of Vague Information Using Fuzzy Similarity Relations

- Computer ScienceFinite Versus Infinite
- 2000

This work uses a similarity relation which is only reflexive, relaxing therefore the properties of symmetry and transitivity to approximate a given set represented by objects having the same description in terms of decision attributes by means of an indiscernibility binary relation.

### Similarity Relation as a Basis for Rough Approximations

- Computer Science
- 1995

A general framework for similarity relations on objects described by a set of attributes is given and the proposed construction of a similarity measure takes into account both positive and negative contributions to the credibility of the similarity (concordance and discordance).

### Putting Rough Sets and Fuzzy Sets Together

- Computer ScienceIntelligent Decision Support
- 1992

In this paper we argue that fuzzy sets and rough sets aim to different purposes and that it is more natural to try to combine the two models of uncertainty (vagueness for fuzzy sets and coarseness…

### On the Unknown Attribute Values in Learning from Examples

- Computer ScienceISMIS
- 1991

This paper shows that the existing approaches to learning from inconsistent examples are not sufficient, and a new method is suggested, which transforms the original decision table with unknown values into a new decision table in which every attribute value is known.

### On rough set based approaches to induction of decision rules

- Computer Science
- 1998

This paper discusses problems connected with using the rough set theory in induction of decision rules if the input data table contains inconsisten cies and presents algorithms that induce all three distinguished categories of the sets of decisionRules.

### ROUGH FUZZY SETS AND FUZZY ROUGH SETS

- Computer Science
- 1990

It is argued that both notions of a rough set and a fuzzy set aim to different purposes, and it is more natural to try to combine the two models of uncertainty (vagueness and coarseness) rather than to have them compete on the same problems.

### Valued Tolerance and Decision Rules

- Computer ScienceRough Sets and Current Trends in Computing
- 2000

The concept of valued tolerance is introduced as an extension of the usual concept of indiscernibility (which is a crisp equivalence relation) in rough sets theory and its use in classification problems is discussed.

### Fuzzy Similarity Relation as a Basis for Rough Approximations

- Computer ScienceRough Sets and Current Trends in Computing
- 1998

The rough sets theory was originally founded on the idea of approximating a given set by means of indiscernibility binary relation, which was assumed to be an equivalence relation (reflexive, symmetric and transitive), but now the assumption of symmetry and transitivity is relaxed.