Granular box regression

Journal article

Peters, Georg. (2011). Granular box regression. IEEE Transactions on Fuzzy Systems. 19(6), pp. 1141 - 1152. https://doi.org/10.1109/TFUZZ.2011.2162416
AuthorsPeters, Georg
Abstract

Granular computing (GrC) has gained increasing attention in the past decade. Although not uniquely defined, its basic idea is to approximate detailed machine-like information by a coarser presentation on a human-like level. Within granular computing, the mapping of continuous variables into intervals plays an important role. These intervals are often prerequisites for the formulation of linguistic variables. In this paper, we suggest a piecewise interval approximation and propose granular box regression. Its objective is to establish relationships between independent and dependent variables by multidimensional boxes. We interpret granular box regression as interval regression and show its potential for the extraction of fuzzy rules from data. In two experiments, we apply granular box regression to an artificial as well as to a real dataset in the field of finance and evaluate its properties.

Keywordsapproximation methods; clustering algorithms; minimization; regression analysis
Year2011
JournalIEEE Transactions on Fuzzy Systems
Journal citation19 (6), pp. 1141 - 1152
PublisherInstitute of Electrical and Electronics Engineers
ISSN1063-6706
Digital Object Identifier (DOI)https://doi.org/10.1109/TFUZZ.2011.2162416
Scopus EID2-s2.0-82455164453
Page range1141 - 1152
Research GroupSchool of Arts
Publisher's version
File Access Level
Controlled
Place of publicationUnited States
Permalink -

https://acuresearchbank.acu.edu.au/item/85vx3/granular-box-regression

Log in to edit

Restricted files

Publisher's version

  • 118
    total views
  • 0
    total downloads
  • 11
    views this month
  • 0
    downloads this month
These values are for the period from 19th October 2020, when this repository was created.

Export as

Related outputs

Credit scoring using three-way decisions with probabilistic rough sets
Maldonado, Sebastian, Peters, Georg and Weber, Richard. (2020). Credit scoring using three-way decisions with probabilistic rough sets. Information Sciences. 507, pp. 700 - 714. https://doi.org/10.1016/j.ins.2018.08.001
A computer-based framework supporting education in STEM subjects
Peters, Georg, Rueckert, Tom and Seruga, Jan. (2019). A computer-based framework supporting education in STEM subjects. In In Hammoudi, Slimane, Smialek, Michal, Camp, Oliver and Filipe, Joaquim (Ed.). Enterprise information systems pp. 1-21 Springer Nature. https://doi.org/10.1007/978-3-030-26169-6_1
dynXcube – categorizing dynamic data analysis
Peters, Georg and Weber, Richard. (2018). dynXcube – categorizing dynamic data analysis. Information Sciences. 463-464, pp. 21 - 32. https://doi.org/10.1016/j.ins.2018.06.026
A framework supporting literacy in mathematics and software programming
Peters, Georg, Rueckert, Tom and Seruga, Jan. (2018). A framework supporting literacy in mathematics and software programming. Portugal: Scitepress. pp. 497 - 506 https://doi.org/10.5220/0006629304970506
Some potentials of the R-Project Environment for teachers’ and students’ education in mathematics, algorithms’ programming and dynamic website development
Peters, Georg, Rueckert, Tom and Seruga, Jan. (2018). Some potentials of the R-Project Environment for teachers’ and students’ education in mathematics, algorithms’ programming and dynamic website development. United States of America: Association for he Advancement of Computing in Education (AACE). pp. 1816 - 1821
DCC : A framework for dynamic granular clustering
Peters, Georg and Weber, Richard. (2016). DCC : A framework for dynamic granular clustering. Granular Computing. 1, pp. 1-11. https://doi.org/10.1007/s41066-015-0012-z
A supply sided analysis of leading MooC platforms and universities
Peters, Georg and Seruga, Jan. (2016). A supply sided analysis of leading MooC platforms and universities. Knowledge Management and E-Learning. 8(1), pp. 158 - 181.
A comparative analysis of MOOC - Australia's position in the international education market
Peters, Georg, Sacker, Doreen and Seruga, Jan. (2015). A comparative analysis of MOOC - Australia's position in the international education market. Australasian Conference on Information Systems. Australia: University of South Australia. pp. 1 - 10
Is there any need for rough clustering?
Peters, Georg. (2015). Is there any need for rough clustering? Pattern Recognition Letters. 53, pp. 31 - 37. https://doi.org/10.1016/j.patrec.2014.11.003
Assessing rough classifiers
Peters, Georg. (2015). Assessing rough classifiers. Fundamenta Informaticae. 137, pp. 493 - 515. https://doi.org/10.3233/FI-2015-1191
Analysis of user-weighted pi rough k-means
Peters, Georg and Lingras, Pawan. (2014). Analysis of user-weighted pi rough k-means. In D Miao, W Pedrycz and D Slezak (Ed.). Rough Sets and Knowledge Technology. Switzerland: Springer. pp. 547 - 556 https://doi.org/10.1007/978-3-319-11740-9_50
Tweeting politicians: An analysis of the usage of a micro blogging system
Roth, Matthias, Peters, Georg and Seruga, Jan. (2014). Tweeting politicians: An analysis of the usage of a micro blogging system. In S. Hammoudi, J. Cordeiro and L. A. Maciaszek & J. Filipe (Ed.). Cham, Switzerland: Springer. pp. 351 - 364 https://doi.org/10.1007/978-3-319-09492-2_21
Rough clustering utilising the principle of indifference
Peters, Georg. (2014). Rough clustering utilising the principle of indifference. Information Sciences. 277, pp. 358 - 374. https://doi.org/10.1016/j.ins.2014.02.073
An illustrative comparison of rough k-Means to classical clustering approaches
Peters, Georg and Crespo, Fernando. (2013). An illustrative comparison of rough k-Means to classical clustering approaches. In D Ciucci, M Inuiguchi and Y Yao (Ed.). Rough Sets, Fuzzy Sets, Data Mining and Granular Computing. Germany: Springer. pp. 337 - 344
Soft clustering: Fuzzy and rough approaches and their extensions and derivatives
Peters, Georg, Crespo, Fernando, Lingas, Pawan and Weber, Richard. (2013). Soft clustering: Fuzzy and rough approaches and their extensions and derivatives. International Journal of Approximate Reasoning. 54(2), pp. 307 - 322. https://doi.org/10.1016/j.ijar.2012.10.003
Some insights into the role of social media in political communication
Roth, Matthias, Peters, Georg and Seruga, Jan. (2013). Some insights into the role of social media in political communication. In S Hammoudi, L Maciaszek and J Cordeiro (Ed.). Proceedings of the 15th International Conference on Enterprise Information Systems. France: Institute for Systems and Technologies of Information, Control and Communication. pp. 353 - 362 https://doi.org/10.5220/0004418603510360
Dynamic clustering with soft computing
Peters, Georg and Weber, Richard. (2012). Dynamic clustering with soft computing. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 2(3), pp. 226 - 236. https://doi.org/10.1002/widm.1050
Tackling outliers in granular box regression
Peters, Georg and Lacic, Zdravko. (2012). Tackling outliers in granular box regression. Information Sciences. 212, pp. 44 - 56. https://doi.org/10.1016/j.ins.2012.05.006
Current trends in product lifecycle management
Staisch, Adam, Peters, Georg, Stueckl, Thomas and Seruga, Jan. (2012). Current trends in product lifecycle management. In J Lamp (Ed.). Proceedings of the 23rd Australasian Conference on Information Systems. Geelong, Victoria, Australia: Deakin University Press. pp. 1 - 10
Network Effects in the ERP Systems Market : An Analysis of the Implications of Business Intelligence and Cloud Computing
Peters, Georg and Seruga, Jan. (2012). Network Effects in the ERP Systems Market : An Analysis of the Implications of Business Intelligence and Cloud Computing. International Journal of Advanced Science and Technology. 43, pp. 105 - 114.
Network Effects in the ERP Systems Market: An Analysis of the Implications of Business Intelligence and Cloud Computing
Peters, Georg and Seruga, Jan. (2012). Network Effects in the ERP Systems Market: An Analysis of the Implications of Business Intelligence and Cloud Computing. International Journal of Advanced Science and Technology. 43, pp. 105 - 114.
Dynamic rough clustering and its applications
Peters, Georg, Weber, Richard and Nowatzke, René. (2012). Dynamic rough clustering and its applications. Applied Soft Computing Journal. 12(10), pp. 3193 - 3207. https://doi.org/10.1016/j.asoc.2012.05.015
Cross media and e-publishing
Rogobete, Carina, Peters, Georg and Seruga, Jan. (2012). Cross media and e-publishing. International Journal of u- and e- Service, Science and Technology. 5(2), pp. 17 - 29.
Rough clustering
Lingras, Pawan and Peters, Georg. (2011). Rough clustering. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 1(1), pp. 64 - 72. https://doi.org/10.1002/widm.16
The effectiveness of electronic communication
Peters, Georg, Seruga, Jan and Zellmer, V.. (2011). The effectiveness of electronic communication. In B. White, P. Isaias and F. M. Santoro (Ed.). Proceedings of the IADIS International Conference WWW/Internet 2011. Brazil: IADIS Press. pp. 616 - 619
Analyzing IT business values – A Dominance based Rough Sets Approach perspective
Peters, Georg and Poon, Simon. (2011). Analyzing IT business values – A Dominance based Rough Sets Approach perspective. Expert Systems with Applications. 38(9), pp. 11120 - 11128. https://doi.org/10.1016/j.eswa.2011.02.157