Rule generation in Rough set Non-deterministic Information Analysis (RNIA) and some applications of the obtained rules
We submitted this paper to the special issue, “Four Decades of Rough Set Theory: Achievements and Future.” We have researched a theory and an execution tool for rule generation from a Deterministic Information System (DIS) and a Non-deterministic Information System (NIS). We developed the NIS-Aprior...
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| Published in | Applied soft computing Vol. 172; p. 112842 |
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| Format | Journal Article |
| Language | English |
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01.03.2025
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| Abstract | We submitted this paper to the special issue, “Four Decades of Rough Set Theory: Achievements and Future.” We have researched a theory and an execution tool for rule generation from a Deterministic Information System (DIS) and a Non-deterministic Information System (NIS). We developed the NIS-Apriori algorithm, which combines the rough sets-based concept and the Apriori algorithm, for rule generation from NIS. We term this research series as a Rough set Non-deterministic Information Analysis (RNIA). In the first half of this paper, we describe the framework of RNIA and its execution environment, as well as the results we achieved. Then, later in this paper, we enumerate various applications of RNIA, such as detection of data dependencies, decision support, estimation and completion of missing values, the problem of learning DIS from NIS, and generation of rules from non-tabular and multiple heterogeneous data sets. They are our current and prospective subjects. RNIA’s capabilities can lead to several developments.
•Our past research on RNIA is reviewed with its execution videos.•Attribute dependency (or feature selection) analysis using the obtained rules is studied.•Solutions to Missing value imputation and Machine Learning by Rule Generation (MLRG) are presented with their execution videos.•A Descriptor-based Information System (DbIS) for rule generation from non-tabular data sets is studied. |
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| AbstractList | We submitted this paper to the special issue, “Four Decades of Rough Set Theory: Achievements and Future.” We have researched a theory and an execution tool for rule generation from a Deterministic Information System (DIS) and a Non-deterministic Information System (NIS). We developed the NIS-Apriori algorithm, which combines the rough sets-based concept and the Apriori algorithm, for rule generation from NIS. We term this research series as a Rough set Non-deterministic Information Analysis (RNIA). In the first half of this paper, we describe the framework of RNIA and its execution environment, as well as the results we achieved. Then, later in this paper, we enumerate various applications of RNIA, such as detection of data dependencies, decision support, estimation and completion of missing values, the problem of learning DIS from NIS, and generation of rules from non-tabular and multiple heterogeneous data sets. They are our current and prospective subjects. RNIA’s capabilities can lead to several developments.
•Our past research on RNIA is reviewed with its execution videos.•Attribute dependency (or feature selection) analysis using the obtained rules is studied.•Solutions to Missing value imputation and Machine Learning by Rule Generation (MLRG) are presented with their execution videos.•A Descriptor-based Information System (DbIS) for rule generation from non-tabular data sets is studied. |
| ArticleNumber | 112842 |
| Author | Watada, Junzo Sakai, Hiroshi Nakata, Michinori Ślęzak, Dominik |
| Author_xml | – sequence: 1 givenname: Hiroshi orcidid: 0000-0001-7847-030X surname: Sakai fullname: Sakai, Hiroshi email: sakai-hi@shimonoseki-cu.ac.jp organization: Department of Data Science, Shimonoseki City University, 2-1-1 Daigakucho, Shimonoseki, Yamaguchi, 751-8510, Japan – sequence: 2 givenname: Michinori orcidid: 0000-0001-5488-3150 surname: Nakata fullname: Nakata, Michinori email: nakatam@ieee.org organization: Tokyo University of Information Sciences, 4-1 Onaridai, Wakaba-ku, Chiba, 265-8501, Japan – sequence: 3 givenname: Dominik orcidid: 0000-0003-2453-4974 surname: Ślęzak fullname: Ślęzak, Dominik email: slezak@mimuw.edu.pl organization: Institute of Informatics, University of Warsaw, Banacha 2, Warszawa, 02-097, Poland – sequence: 4 givenname: Junzo orcidid: 0000-0002-3322-2086 surname: Watada fullname: Watada, Junzo email: watada-ju@shimonoseki-cu.ac.jp organization: Department of Data Science, Shimonoseki City University, 2-1-1 Daigakucho, Shimonoseki, Yamaguchi, 751-8510, Japan |
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| Cites_doi | 10.1016/j.ins.2009.09.021 10.1016/S0377-2217(98)00127-1 10.1145/322234.322239 10.1016/j.ijar.2007.05.001 10.1016/0022-0000(93)90048-2 10.1016/j.ins.2018.09.008 10.1016/j.ijar.2009.02.003 10.1016/0304-3975(76)90077-3 10.1016/0304-3975(84)90010-0 10.1016/j.ins.2006.06.009 10.1016/j.ins.2009.11.023 10.1016/S0020-0255(98)10065-8 10.1145/1559845.1559933 10.1007/BF01001956 10.1111/1468-0394.00252 10.1080/17451000.2013.798898 10.14778/3204028.3204032 10.1145/320083.320088 10.1016/S0020-0255(98)10019-1 10.1016/S0020-0255(99)00065-1 10.1145/362384.362685 10.1016/j.ins.2014.07.029 10.1007/978-1-0716-2628-3_256 10.1016/j.ijar.2007.10.006 10.1109/BigData47090.2019.9006420 |
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| Keywords | Missing value imputation Rule generation Rough sets Descriptor-based information systems Applications of the obtained rules |
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| Title | Rule generation in Rough set Non-deterministic Information Analysis (RNIA) and some applications of the obtained rules |
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