Abstract

We report results of experiments on several data sets, in particular: Monk's problems data (see [58]), medical data (lymphography, breast cancer, primary tumor - see [30]) and StatLog's data (see [32]). We compare standard methods for extracting laws from decision tables (see [43], [52]), based on rough set (see [42]) and boolean reasoning (see [8]), with the method based on dynamic reducts and dynamic rules (see [3],[4],[5],[6]). We also compare the results of computer experiments on those data sets obtained by applying our system based on rough set methods with the results on the same data sets obtained with help of several data analysis systems known from literature.

Citations

682	The CN2 induction algorithm - Clark, Niblett - 1989
590	The cascade-correlation learning architecture - Fahlman, Lebiere - 1990
409	Fast discovery of association rules - Agrawal, Mannila, et al. - 1996
395	Theory of Point Estimation - Lehmann - 1983
296	The multi-purpose incremental learning system aq15 and its testing application to three medical domains - Michalski, Mozetic, et al. - 1986
277	LA: A practical approach to feature selection - Kira, Rendell - 1992
187	Learning with many irrelevant features. In - Almuallim, Dietterich - 1991
148	Estimating the error rate of a prediction rule: improvement on cross-validation - Efron - 1983
79	Boolean Reasoning - Brown - 1990
68	Feature selection and extraction - Kittler - 1986
58	Handling noise in inductive logic programming - Dzeroski - 1991
55	LERS—A System for Learning from Examples Based on Rough Sets - Grzymala-Busse - 1992
45	Dynamic reducts as a tool for extracting laws from decision tables - Bazan, Skowron, et al.
41	Assistant-86: A Knowledge Elicitation Tool for Sophisticated Users - Cestnik, Konenenko, et al. - 1987
38	Statistical Theory and Methodology in Science and Engineering - BROWNLEE - 1960
35	C.: Machine learning, neural and statistical classification. Ellis Horwood - Michie, Spiegelhalter, et al. - 1994
32	Automatic feature generation for problem solving systems - Fawcett, Utgoff - 1992
29	Probability Theory and Mathematical Statistics - Fisz - 1963
25	Data driven constructive induction in aq17-pre: A method and experiments - Bloedorn, Michalski - 1991
20	Quantization of real-valued attributes - Nguyen, Skowron - 1995
11	Searching for features defined by hyperplanes - Nguyen, Nguyen, et al. - 1996
10	SMART User's Guide - Friedman - 1984
8	P.: Discovery of Decision Rules from Experimental Data - Bazan, Skowron, et al. - 1994
8	Rough Family — Software Implementation of Rough Set Based Data Analysis and Rule Discovery Techniques - Mienko, Slowinski, et al. - 1996
7	Market data analysis: a rough set approach - Bazan, Skowron, et al. - 1994
6	Dynamic reducts and statistical inference - Bazan - 1996
6	A new version of the rule induction system LERS. Fundamenta Informaticae 31 - Grzymala-Busse - 1997
6	Inductive Logic Programming - Muggelton - 1992
5	Recognition of handwritten British postal addresses - Downton, Tregidgo, et al. - 1992
4	Constructive Induction: An Automated Improvement of Knowledge Representation Spaces for Machine Learning - Michalski, Wnek - 1993
3	A Rough Set Approach to Default Rules Data Mining. Forthcoming - Mollestad - 1996
3	K.: Applications of rough sets, neural networks and maximum likelihood for texture classification based on singular value decomposition - Nguyen, Swiniarski, et al. - 1994
2	eds.): Machine learning 3 - Kodratoff, Michalski - 1990
1	PRISM: An algorithm for inducing modular rules - Cedrowska - 1988
1	P.: The induction of probabilistic rule sets - the algorithm - Goodman, Smyth - 1989
1	S.: Introduction to statistical inference. Prinston - Keeping - 1962