On Knowledge Granulation and Applications to Classifier Induction in the Framework of Rough Mereology
Knowledge granulation as proposed by Zadeh consists in making objects under discussion into classes called granules; objects within a granule are similar one to another to a satisfactory degree relative to a chosen similarity measure. Rough mereology as developed by Polkowski in a series of works is...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2009-12-01
|
| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://www.atlantis-press.com/article/1890.pdf |
| _version_ | 1818008041362030592 |
|---|---|
| author | Lech Polkowski Piotr Artiemjew |
| author_facet | Lech Polkowski Piotr Artiemjew |
| author_sort | Lech Polkowski |
| collection | DOAJ |
| description | Knowledge granulation as proposed by Zadeh consists in making objects under discussion into classes called granules; objects within a granule are similar one to another to a satisfactory degree relative to a chosen similarity measure. Rough mereology as developed by Polkowski in a series of works is especially suited to tasks of granulation as it does propose a systematic way to construct similarity measures in data sets and offers as well theoretic tools for granule formation in the form of an adaptation of the idea of mereological classes defined by Le´sniewski in his mereology theory. In this article, which extends our contributions to the Special Session on Rough Mereology organized by Polkowski and Artiemjew as a part of the Conference on Rough Sets and Knowledge Technology RSKT 2008, we give a fairly detailed account of basic ideas of rough mereology, a description of basic similarity measures called rough inclusions along with the idea of granulated data sets (granular reflections of data sets); then we follow with the idea on how to construct classifiers from granular data, and finally we present some results of granular classification on real data sets. In what follows, we restrict ourselves to a closed world of a given decision system, leaving aside metaphysical questions of relations between this system and the overwhelming universe of all feasible objects. |
| first_indexed | 2024-04-14T05:23:55Z |
| format | Article |
| id | doaj.art-d959be5488564328b49c5a379dea67dc |
| institution | Directory Open Access Journal |
| issn | 1875-6883 |
| language | English |
| last_indexed | 2024-04-14T05:23:55Z |
| publishDate | 2009-12-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj.art-d959be5488564328b49c5a379dea67dc2022-12-22T02:10:03ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832009-12-012410.2991/ijcis.2009.2.4.1On Knowledge Granulation and Applications to Classifier Induction in the Framework of Rough MereologyLech PolkowskiPiotr ArtiemjewKnowledge granulation as proposed by Zadeh consists in making objects under discussion into classes called granules; objects within a granule are similar one to another to a satisfactory degree relative to a chosen similarity measure. Rough mereology as developed by Polkowski in a series of works is especially suited to tasks of granulation as it does propose a systematic way to construct similarity measures in data sets and offers as well theoretic tools for granule formation in the form of an adaptation of the idea of mereological classes defined by Le´sniewski in his mereology theory. In this article, which extends our contributions to the Special Session on Rough Mereology organized by Polkowski and Artiemjew as a part of the Conference on Rough Sets and Knowledge Technology RSKT 2008, we give a fairly detailed account of basic ideas of rough mereology, a description of basic similarity measures called rough inclusions along with the idea of granulated data sets (granular reflections of data sets); then we follow with the idea on how to construct classifiers from granular data, and finally we present some results of granular classification on real data sets. In what follows, we restrict ourselves to a closed world of a given decision system, leaving aside metaphysical questions of relations between this system and the overwhelming universe of all feasible objects.https://www.atlantis-press.com/article/1890.pdfrough setsknowledge granulationrough mereologyrough inclusionsclassification of data into categoriesgranular data sets and classifiers |
| spellingShingle | Lech Polkowski Piotr Artiemjew On Knowledge Granulation and Applications to Classifier Induction in the Framework of Rough Mereology International Journal of Computational Intelligence Systems rough sets knowledge granulation rough mereology rough inclusions classification of data into categories granular data sets and classifiers |
| title | On Knowledge Granulation and Applications to Classifier Induction in the Framework of Rough Mereology |
| title_full | On Knowledge Granulation and Applications to Classifier Induction in the Framework of Rough Mereology |
| title_fullStr | On Knowledge Granulation and Applications to Classifier Induction in the Framework of Rough Mereology |
| title_full_unstemmed | On Knowledge Granulation and Applications to Classifier Induction in the Framework of Rough Mereology |
| title_short | On Knowledge Granulation and Applications to Classifier Induction in the Framework of Rough Mereology |
| title_sort | on knowledge granulation and applications to classifier induction in the framework of rough mereology |
| topic | rough sets knowledge granulation rough mereology rough inclusions classification of data into categories granular data sets and classifiers |
| url | https://www.atlantis-press.com/article/1890.pdf |
| work_keys_str_mv | AT lechpolkowski onknowledgegranulationandapplicationstoclassifierinductionintheframeworkofroughmereology AT piotrartiemjew onknowledgegranulationandapplicationstoclassifierinductionintheframeworkofroughmereology |