publication . Preprint . 2016

Finding Statistically Significant Attribute Interactions

Henelius, Andreas; Ukkonen, Antti; Puolamäki, Kai;
Open Access English
  • Published: 22 Dec 2016
Abstract
Comment: 9 pages, 4 tables, 1 figure
Subjects
free text keywords: Statistics - Machine Learning, Computer Science - Learning
Funded by
AKA| Human-guided data analysis
Project
  • Funder: Academy of Finland (AKA)
  • Project Code: 288814
Download from
22 references, page 1 of 2

[1] R. Agrawal and R. Srikant. Privacy-preserving data mining. In SIGMOD 2000, pages 439-450, 2000.

[2] K. Bache and M. Lichman. UCI machine learning repository, 2014.

[3] R. J. Bayardo Jr. and R. Srikant. Technological solutions for protecting privacy. IEEE Computer, 36(9):115-118, 2003. [OpenAIRE]

[4] C. Bucilã, R. Caruana, and A. Niculescu-Mizil. Model compression. In KDD 2006, pages 535-541, 2006.

[5] M. Fernández-Delgado, E. Cernadas, S. Barro, and D. Amorim. Do we need hundreds of classifiers to solve real world classification problems? Journal of Machine Learning Research, 15:3133-3181, 2014.

[6] A. A. Freitas. Understanding the crucial role of attribute interaction in data mining. Artificial Intelligence Review, 16(3):177-199, 2001.

[7] I. Guyon and A. Elisseeff. An introduction to variable and feature selection. Journal of machine learning research, 3(Mar):1157-1182, 2003. [OpenAIRE]

[8] A. Henelius, K. Puolamäki, H. Boström, L. Asker, and P. Papapetrou. A peek into the black box: exploring classifiers by randomization. Data mining and knowledge discovery, 28(5-6):1503-1529, 2014. [OpenAIRE]

[9] A. Henelius, K. Puolamäki, I. Karlsson, J. Zhao, L. Asker, H. Boström, and P. Papapetrou. Goldeneye++: A closer look into the black box. In Statistical Learning and Data Sciences, pages 96-105. Springer, 2015.

[10] A. Jakulin and I. Bratko. Analyzing attribute dependencies. In PKDD 2003, pages 229-240. Springer, 2003. [OpenAIRE]

[11] A. Jakulin and I. Bratko. Testing the significance of attribute interactions. In ICML 2004, 2004. [OpenAIRE]

[12] A. Jakulin, I. Bratko, D. Smrke, J. Demšar, and B. Zupan. Attribute interactions in medical data analysis. In Conference on Artificial Intelligence in Medicine in Europe, pages 229-238. Springer, 2003. [OpenAIRE]

[13] T. J. Koski and J. M. Noble. A review of bayesian networks and structure learning. Annales Societatis Mathematicae Polonae. Series 3: Mathematica Applicanda, 40(1):53-103, 2012.

[14] A. Liaw and M. Wiener. Classification and regression by randomforest. R News, 2(3):18-22, 2002.

[15] M. Mampaey and J. Vreeken. Summarizing categorical data by clustering attributes. Data Mining and Knowledge Discovery, 26(1):130-173, 2013.

22 references, page 1 of 2
Abstract
Comment: 9 pages, 4 tables, 1 figure
Subjects
free text keywords: Statistics - Machine Learning, Computer Science - Learning
Funded by
AKA| Human-guided data analysis
Project
  • Funder: Academy of Finland (AKA)
  • Project Code: 288814
Download from
22 references, page 1 of 2

[1] R. Agrawal and R. Srikant. Privacy-preserving data mining. In SIGMOD 2000, pages 439-450, 2000.

[2] K. Bache and M. Lichman. UCI machine learning repository, 2014.

[3] R. J. Bayardo Jr. and R. Srikant. Technological solutions for protecting privacy. IEEE Computer, 36(9):115-118, 2003. [OpenAIRE]

[4] C. Bucilã, R. Caruana, and A. Niculescu-Mizil. Model compression. In KDD 2006, pages 535-541, 2006.

[5] M. Fernández-Delgado, E. Cernadas, S. Barro, and D. Amorim. Do we need hundreds of classifiers to solve real world classification problems? Journal of Machine Learning Research, 15:3133-3181, 2014.

[6] A. A. Freitas. Understanding the crucial role of attribute interaction in data mining. Artificial Intelligence Review, 16(3):177-199, 2001.

[7] I. Guyon and A. Elisseeff. An introduction to variable and feature selection. Journal of machine learning research, 3(Mar):1157-1182, 2003. [OpenAIRE]

[8] A. Henelius, K. Puolamäki, H. Boström, L. Asker, and P. Papapetrou. A peek into the black box: exploring classifiers by randomization. Data mining and knowledge discovery, 28(5-6):1503-1529, 2014. [OpenAIRE]

[9] A. Henelius, K. Puolamäki, I. Karlsson, J. Zhao, L. Asker, H. Boström, and P. Papapetrou. Goldeneye++: A closer look into the black box. In Statistical Learning and Data Sciences, pages 96-105. Springer, 2015.

[10] A. Jakulin and I. Bratko. Analyzing attribute dependencies. In PKDD 2003, pages 229-240. Springer, 2003. [OpenAIRE]

[11] A. Jakulin and I. Bratko. Testing the significance of attribute interactions. In ICML 2004, 2004. [OpenAIRE]

[12] A. Jakulin, I. Bratko, D. Smrke, J. Demšar, and B. Zupan. Attribute interactions in medical data analysis. In Conference on Artificial Intelligence in Medicine in Europe, pages 229-238. Springer, 2003. [OpenAIRE]

[13] T. J. Koski and J. M. Noble. A review of bayesian networks and structure learning. Annales Societatis Mathematicae Polonae. Series 3: Mathematica Applicanda, 40(1):53-103, 2012.

[14] A. Liaw and M. Wiener. Classification and regression by randomforest. R News, 2(3):18-22, 2002.

[15] M. Mampaey and J. Vreeken. Summarizing categorical data by clustering attributes. Data Mining and Knowledge Discovery, 26(1):130-173, 2013.

22 references, page 1 of 2
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