Book ChapterDOI
Dynamic Reducts as a Tool for Extracting Laws from Decisions Tables
Jan G. Bazan,Andrzej Skowron,Piotr Synak +2 more
- pp 346-355
TLDR
The results are showing that dynamic reducts can help to extract laws from decision tables, e.g. market data, medical data, textures and handwritten digits.Abstract:
We apply rough set methods and boolean reasoning for knowledge discovery from decision tables. It is not always possible to extract general laws from experimental data by computing first all reducts [12] of a decision table and next decision rules on the basis of these reducts. We investigate a problem how information about the reduct set changes in a random sampling process of a given decision table could be used to generate these laws. The reducts stable in the process of decision table sampling are called dynamic reducts. Dynamic reducts define the set of attributes called the dynamic core. This is the set of attributes included in all dynamic reducts. The set of decision rules can be computed from the dynamic core or from the best dynamic reducts. We report the results of experiments with different data sets, e.g. market data, medical data, textures and handwritten digits. The results are showing that dynamic reducts can help to extract laws from decision tables.read more
Citations
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Journal ArticleDOI
Machine learning
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Journal ArticleDOI
Rough sets theory for multicriteria decision analysis
TL;DR: The original rough set approach proved to be very useful in dealing with inconsistency problems following from information granulation, but is failing when preference-orders of attribute domains (criteria) are to be taken into account and it cannot handle inconsistencies following from violation of the dominance principle.
Journal ArticleDOI
Rough set methods in feature selection and recognition
TL;DR: The algorithm for feature selection is based on an application of a rough set method to the result of principal components analysis (PCA) used for feature projection and reduction.
Journal ArticleDOI
Rough set approach to knowledge-based decision support
Zdzisław Pawlak,Zdzisław Pawlak +1 more
TL;DR: Basic concepts of rough set theory will be outlined and its possible application will be briefly discussed and further research problems will conclude the paper.
Journal ArticleDOI
Semantics-preserving dimensionality reduction: rough and fuzzy-rough-based approaches
Richard Jensen,Qiang Shen +1 more
TL;DR: This paper reviews those techniques that preserve the underlying semantics of the data, using crisp and fuzzy rough set-based methodologies, and several approaches to feature selection based on rough set theory are experimentally compared.
References
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Book
C4.5: Programs for Machine Learning
TL;DR: A complete guide to the C4.5 system as implemented in C for the UNIX environment, which starts from simple core learning methods and shows how they can be elaborated and extended to deal with typical problems such as missing data and over hitting.
Journal ArticleDOI
Machine learning
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Programs for Machine Learning
Steven L. Salzberg,Alberto Segre +1 more
TL;DR: In his new book, C4.5: Programs for Machine Learning, Quinlan has put together a definitive, much needed description of his complete system, including the latest developments, which will be a welcome addition to the library of many researchers and students.
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