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1

A Rough Set Approach to Information Systems Decomposition

Pancerz K., Suraj Z.

Fundamenta Informaticae

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2013

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Vol. 127, nr 1-4

257--272

EN

The aim of this paper is to present the methods and algorithms of information systems decomposition. In the paper, decomposition with respect to reducts and the so-called global decomposition are considered. Moreover, coverings of information systems by components are discussed. An essential difference between two kinds of decomposition can be observed. In general, global decomposition can deliver more components of a given information system. This fact can be treated as some kind of additional knowledge about the system. The proposed approach is based on rough set theory. To demonstrate the usefulness of this approach, we present an illustrative example coming from the economy domain. The discussed decomposition methods can be applied e.g. for design and analysis of concurrent systems specified by information systems, for automatic feature extraction, as well as for control design of systems represented by experimental data tables.

2

Greedy Algorithms for Construction of Approximate Tests for Decision Tables with Many-Valued Decisions

Azad M., Chikalov I., Moshkov M., Zielosko B.

Fundamenta Informaticae

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2012

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Vol. 120, nr 3/4

231-242

EN

The paper is devoted to the study of a greedy algorithm for construction of approximate tests (super-reducts). This algorithm is applicable to decision tables with many-valued decisions where each row is labeled with a set of decisions. For a given row, we should find a decision from the set attached to this row. We consider bounds on the precision of this algorithm relative to the cardinality of tests.

3

Feature Selection Algorithm for Multiple Classifier Systems: A Hybrid Approach

Delimata P., Suraj Z.

Fundamenta Informaticae

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2008

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Vol. 85, nr 1-4

97-110

EN

Many problems in pattern classification and knowledge discovery require a selection of a subset of attributes or features to represent the patterns to be classified. The approach presented in this paper is designed mostly for multiple classifier systems with homogeneous (identical) classifiers. Such systems require many different subsets of the data set. The problem of finding the best subsets of a given feature set is of exponential complexity. The main aim of this paper is to present ways to improve RBFS algorithm which is a feature selection algorithm. RBFS algorithm is computationally quite complex because it uses all decision-relative reducts of a given decision table. In order to increase its speed, we propose a new algorithm called ARS algorithm. The task of this algorithm is to decrease the number of the decision-relative reducts for a decision table. Experiments have shown that ARS has greatly improved the execution time of the RBFS algorithm. A small loss on the classification accuracy of the multiple classifier used on the subset created by this algorithm has also been observed. To improve classification accuracy the simplified version of the bagging algorithm has been applied. Algorithms have been tested on some benchmarks.

4

A New Rough Sets Model Based on Database Systems

Hu X., Lin T.Y., Han J.

Fundamenta Informaticae

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2004

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Vol. 59, nr 2,3

135--152

EN

Rough sets theory was proposed by Pawlak in the early 1980?s and has been applied successfully in a lot of domains. One of the major limitations of the traditional rough sets model in the real applications is the inefficiency in the computation of core and reduct, because all the intensive computational operations are performed in flat files. In order to improve the efficiency of computing core attributes and reducts, many novel approaches have been developed, some of which attempt to integrate database technologies. In this paper, we propose a new rough sets model and redefine the core attributes and reducts based on relational algebra to take advantages of the very efficient set-oriented database operations. With this new model and our new definitions, we present two new algorithms to calculate core attributes and reducts for feature selections. Since relational algebra operations have been efficiently implemented in most widely-used database systems, the algorithms presented in this paper can be extensively applied to these database systems and adapted to a wide range of real-life applications with very large data sets. Compared with the traditional rough set models, our model is very efficient and scalable.

5

Effective tests for minimality in reduct generation

Susmaga R.

Foundations of Computing and Decision Sciences

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1998

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Vol. 23, No. 4

219-240

EN

The paper addresses the problem of checking for inclusion minimality in attribute reduction. Reduction of attributes information/decision tables is an important aspect of table analysis where so called reducts of attributes may successfully be applied. The reducts, however, are hard to generate because of high theoretical complexity of the problem. Especially difficult is the generation of all exact reducts for a given data set. This paper reports on a series of experiments with some advariced algorithms that allow to generate all reducts. Particular attention is paid to a family of algorithms based on the notion of discernibility matrix. The heaviest computing load of these algorithms lies in testing for minimality with regard to inclusion. The paper introduces a new minimality test that makes the algorithms even more effective. All the presented tests are evaluated in experiments with real-life data sets.

6

Computation of shortest reducts

Susmaga R.

Foundations of Computing and Decision Sciences

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1998

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Vol. 23, No. 2

119-137

EN

The paper addresses the problem of computing short reducts in information/decision tables. Reducts in general, and short reducts in particular, may be usefully applied for consitency preserving data size reduction but are hard to find because of high theoretical complexity of the problem. Practical experiments demonstrate, however, that reducts may be successfully computed for many real life data sets using some advanced algorithms. This paper reports on a series of experiments designed to verify not the theoretical complexity but the practical behaviour of algorithms for reduct computation in the average case. In particular, the problem of computing short reducts is sotved by presenting a new algorithm, which is based on the notion of discernibility matrix. All the results of the experiments reported in this paper have been obtained for real-life data sets: