Decision Rule Based Data Models Using NetTRS System Overview

The NetTRS system is a web service that makes induction, evaluation and postprocessing of decision rules possible. The TRS library is the kernel of the system. It allows to induce rules by means of the tolerance rough sets model. The NetTRS makes user interface of the TRS library available in the Internet. The main emphasis of the NetTRS system is placed on induction and postprocessing of decision rules. This article shows the architecture and the functionality of the system. This paper describes also the parameterization of algorithms that are implemented in the TRS library.

[1]  Gholamreza Nakhaeizadeh,et al.  Machine learning and statistics: the interface , 1996 .

[2]  Rafal Latkowski,et al.  DIXER - Distributed Executor for Rough Set Exploration System , 2005, RSFDGrC.

[3]  Andrzej Skowron,et al.  The Discernibility Matrices and Functions in Information Systems , 1992, Intelligent Decision Support.

[4]  Nick Cercone,et al.  Rule Quality Measures for Rule Induction Systems: Description and Evaluation , 2001, Comput. Intell..

[5]  Jaroslaw Stepaniuk,et al.  Optimizations of Rough Set Model , 1998, Fundam. Informaticae.

[6]  Szymon Wilk,et al.  ROSE - Software Implementation of the Rough Set Theory , 1998, Rough Sets and Current Trends in Computing.

[7]  Marek Sikora,et al.  An algorithm for generalization of decision rules by joining , 2005 .

[8]  Jarosław Stepaniuk,et al.  Knowledge discovery by application of rough set models , 2000 .

[9]  Ivan Bratko,et al.  Machine Learning and Data Mining; Methods and Applications , 1998 .

[10]  John Wang,et al.  Data Mining: Opportunities and Challenges , 2003 .

[11]  Inge Bethke,et al.  Equational Constructor Induction , 1996, Fundam. Informaticae.

[12]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.

[13]  Andrzej Skowron,et al.  Chapter 19 the Design and Implementation of a Knowledge Discovery Toolkit Based on Rough Sets { the Rosetta System , 1998 .

[14]  Roman Podraza,et al.  Credibility Coefficients in ARES Rough Set Exploration System , 2005, RSFDGrC.

[15]  Marcin S. Szczuka,et al.  A New Version of Rough Set Exploration System , 2002, Rough Sets and Current Trends in Computing.

[16]  Ian Witten,et al.  Data Mining , 2000 .

[17]  Roman Słowiński,et al.  Intelligent Decision Support , 1992, Theory and Decision Library.

[18]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[19]  J. Ross Quinlan,et al.  Combining Instance-Based and Model-Based Learning , 1993, ICML.

[20]  Sadaaki Miyamoto,et al.  Rough Sets and Current Trends in Computing , 2012, Lecture Notes in Computer Science.

[21]  Jerzy W. Grzymala-Busse,et al.  Data mining based on rough sets , 2003 .

[22]  Marek Sikora,et al.  Rule Quality Measures in Creation and Reduction of Data Rule Models , 2006, RSCTC.

[23]  Z. Pawlak.  Rough Sets: Theoretical Aspects of Reasoning about Data , 1991 .

[24]  Dorothea Heiss-Czedik,et al.  An Introduction to Genetic Algorithms. , 1997, Artificial Life.

[25]  Aiko M. Hormann,et al.  Programs for Machine Learning. Part I , 1962, Inf. Control..

[26]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .