RSES 2.2 Rough Set Exploration System 2.2 With an application implementation

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1 RSES 2.2 Rough Set Exploration System 2.2 With an application implementation A Collection of Tools for Rough Set Computations Software tools produced by: Warsaw University Presented by: Ali Hmer Presentation Outline What is RSES History of RSES Aims and capabilities of RSES Technical requirements Architecture of RSES General scheme of data analysis process Managing Input and Output Data Pre-processing Reduction and Reducts Construction and Utilization of Classifiers 2

2 What is RSES RSES is freely available software system toolset for data exploration, classification support and knowledge discovery. It is used to analyze tabular datasets utilizing various methods. Most of RSES methods are based on Rough Set theory. 3 History It was created as a project accompanying master thesis of Krzysztof Przylucki and Joanna Slupek (1993) supervised by Prof. Andrezej Skowron (University of Warsaw). Written in C++ for Windows 3.1 platform In 1994 first version was released as a text command tool and was written in C++ for HP-UX. In 1996 the RSES-lib 1.0 library of computational methods was put together. Was written in C++ for Unix and Windows. 4

3 History - Continued The next version of RSES-lib, i.e., RSES-lib 2.0 was created in 1998 and 1999, mostly to satisfy the demand for newer and more versatile tool. In 2000 a new version of RSES emerged. This time it was equipped with Graphical User Interface (GUI) for Microsoft Windows 9x/NT/2000/Me. Using C++. This was named version 1.0 The year of 2002 brought the next major version of RSES (version 2.0). This time written in Java 2003 (Version 2.1), 2005 (Version 2.2 RSESlib 3.0) 5 Aims and capabilities of RSES provide a tool for performing experiments on tabular data sets Import of data from text files Visualization and pre-processing of data including, among others, methods for discretization and missing value completion Construction and application of classifiers for both smaller and vast data sets, together with methods for classifier evaluation 6

4 Technical requirements In order to run RSES the following is recommended at least: CPU - Pentium 200 MHz; 128 MB RAM; Java Virtual Machine version 1.4.1; Operating system MS Windows 9x/NT/2000/Me/XP or Linux/i386 (kernel 2.2 or newer). 7 Architecture of RSES Operating System (MS-Windows, Linux) Java Virtual Machine RSES GUI Java Swing RSES-lib 3.0 Java RSES-lib 2.0 C++ 8

5 General scheme of data analysis process Load/Import Data Table Pre-Processing Missing Value Completion Descretization Attribute creation Classifier construction and evaluation Train-and-test Cross-validation Classification of new objects Reducts Reduction Reduct calculation Dynamic reducts Reducts evaluation 9 Managing Input and Output RSES is able to read several tabular data formats. Text file formatted for old version of RSES (RSES1 file format), Rosetta file format Weka systems. RSES2 file format RSES can save and retrieve data entities created during experiment 10

6 Data Pre-processing Examining Data Statistics the user may examine distribution of a single attribute (i.e decision attribute) system is capable of presenting numerical measurements for distribution of values of a single attribute as well as displaying the corresponding histogram. Missing Value Completion Removal of objects with missing values Filling the missing part of data. Analysis of data without taking into account those objects Treating the missing data as information 11 Date Pre-processing - Continued Discretization The algorithm generates a set of cuts These cuts can be then used for transforming a decision table. As a result we obtain the decision table with the same set of attributes, but the attributes have different values RSES has two versions, code-named global and local Creation of New Attributes RSES makes it possible to add an attribute to decision table. This new attribute is created as a weighted sum of selected existing (numerical) attributes. 12

7 Reduction and Reducts Calculating Reducts exhaustive algorithm. Deterministic algorithm Calculating Dynamic Reducts Dynamic reducts are reducts that remain to be such for many sub-tables of the original decision table From Reducts to Rules global rules. local rules 13 Construction and Utilization of Classifiers RSES contains several types of classifiers, all of them follow the scheme of construction, evaluation and usage. train-and-test k-fold cross-validation There are some other methods like LTF-C and MTD-C 14

8 Conclusion RSES will further grow, as authors intend to enrich it by adding newly developed methods and algorithms. They hope that many researchers will find RSES an useful tool for experimenting with data, in particular using methods related to rough sets. 15 References The Rough Set Exploration System, Jan G. Bazan and Marcin Szczuka, Lecture Notes in Computer Science, 37-56, Springer Berlin / Heidelberg, May 2005 RSES and RSESlib - A Collection of Tools for Rough Set Computations, Jan G. Bazan and Marcin Szczuka, Lecture Notes in Computer Science, , Springer Berlin / Heidelberg, January 2001 Alpigini J. J.,Peters J. F.,Skowron A.,Zhong N.(Eds.),Proceedings of 3rd Int. Conf. on Rough Sets and Current Trends in Computing (RSCTC2002), Malvern, PA, Oct LNAI 2475, Springer-Verlag, Berlin, 2002 Skowron A., Polkowski L.(ed.), Rough Sets in Knowledge Discovery 1 & 2, Physica Verlag, Heidelberg, 1998 Bazan J., A Comparison of Dynamic and non-dynamic Rough Set Methods for Extracting Laws from Decision Tables, In [1], Vol. 1, pp Slezak D., Wroblewski J., Classification Algorithms Based on Linear Combinations of Features. Proceedings of PKDD 99, LNAI 1704, Springer-Verlag, Berlin, 1999, pp

9 References Valdes J.J, Barton A.J, Gene Discovery in Leukemia Revisited: A Computational Intelligence Perspective. In: R. Orchard et al. (eds.), Proceedings of IEA/AIE 2004, LNAI 3029, Springer-Verlag, Berlin, 2004, pp Wojna A.G., Center-Based Indexing in Vector and Metric Spaces. Fundamenta Informaticae, Vol. 56(3), 2003, pp RSES 2.2 Online guide 17 Question time?? 18

The Rough Set Exploration System

The Rough Set Exploration System Jan G. Bazan 1 and Marcin Szczuka 2 1 Institute of Mathematics, University of Rzeszów, Rejtana 16A, 35-310 Rzeszów, Poland bazan@univ.rzeszow.pl 2 Institute of Mathematics,