Complexity of Rule Sets Induced from Data Sets with Many Lost and Attribute-Concept Values

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• 关键词：Incomplete data ; Lost values ; Attribute ; concept values ; Probabilistic approximations ; MLEM2 rule induction algorithm
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9693
• 期：1
• 页码：27-36
• 全文大小：2,312 KB
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  9.Clark, P.G., Grzymala-Busse, J.W.: Experiments on probabilistic approximations. In: Proceedings of the 2011 IEEE International Conference on Granular Computing, pp. 144–149 (2011)
  10.Clark, P.G., Grzymala-Busse, J.W., Rzasa, W.: Mining incomplete data with singleton, subset and concept approximations. Inf. Sci. 280, 368–384 (2014)MathSciNet CrossRef
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  12.Clark, P.G., Grzymala-Busse, J.W.: Mining incomplete data with lost values and attribute-concept values. In: Proceedings of the IEEE International Conference on Granular Computing, pp. 49–54 (2014)
  13.Clark, P.G., Grzymala-Busse, J.W.: Mining incomplete data with many lost and attribute-concept values. In: Ciucci, D., Wang, G., Mitra, S., Wu, W.-Z. (eds.) RSKT 2015. LNCS, vol. 9436, pp. 100–109. Springer, Heidelberg (2015)CrossRef
  14.Clark, P.G., Grzymala-Busse, J.W.: On the number of rules and conditions in mining incomplete data with lost values and attribute-concept values. In: Proceedings of the DBKDA 7-th International Conference on Advances in Databases, Knowledge, and Data Applications, pp. 121–126 (2015)
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• 作者单位：Patrick G. Clark (19)
  Cheng Gao (19)
  Jerzy W. Grzymala-Busse (19) (20)
  
  19. Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS, 66045, USA
  20. Department of Expert Systems and Artificial Intelligence, University of Information Technology and Management, 35-225, Rzeszow, Poland
  
• 丛书名：Artificial Intelligence and Soft Computing
• ISBN：978-3-319-39384-1
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9693

文摘

In this paper we present experimental results on rule sets induced from 12 data sets with many missing attribute values. We use two interpretations of missing attribute values: lost values and attribute-concept values. Our main objective is to check which interpretation of missing attribute values is better from the view point of complexity of rule sets induced from the data sets with many missing attribute values. The better interpretation is the attribute-value. Our secondary objective is to test which of the three probabilistic approximations used for the experiments provide the simplest rule sets: singleton, subset or concept. The subset probabilistic approximation is the best, with 5 % significance level.