Pruning Algorithm for the Minimum Rule Reduct Generation
In this paper we consider the rule reduct generation
problem. Rule Reduct Generation (RG) and Modified Rule
Generation (MRG) algorithms, that are used to solve this problem,
are well-known. Alternative to these algorithms, we develop Pruning
Rule Generation (PRG) algorithm. We compare the PRG algorithm
with RG and MRG.
[1] Z. Pawlak, “Rough Sets”, International Journal of Computer and
Information Sciences, vol. 11, pp. 341-356, 1982
[2] A. Wakulicz-Deja, A and P. Paszek, “Diagnose progressive
encephalopathy applying the rough set theory”, International Journal of
Medical Informatics, vol. 46, pp. 119-127,1997
[3] K. Slowinski, J. Stefanowski and D. Swinski, “Application of rule
induction and rough sets to verification of magnetic resonance
diagnosis”, Fundamenta Informaticae, vol. 53, pp. 345-363, 2002
[4] P. J. Lingras,“ Belief and probability based database mining”, in Proc. of
the Ninth Florida Artificial Intelligence Symposium, 1996, pp. 316-320
[5] A. Mrozek and Ekabek , “Rough sets in economic applications.” in
Rough Sets in Knowledge Discovery 2: Applications, Case Studies and
Software Systems, Polkowski and Skowron, Ed. , Germany, Verlag,
1998, pp. 238-271
[6] Z. Pawlak, ” On conflicts”, International Journal of Man-Machine
Studies, vol. 21, pp. 127-134, 1984
[7] A. Mrozek, and L. Plonka, ”Rough sets in image analysis”, Foundations
of Computing Decision Sciences, vol. 18, pp. 259-273, 1993
[8] G. Griffin and Z. Chen , “Rough set extension of Tcl for data mining”,
Knowledge-Based systems, vol. 11, pp. 249-253, 1998
[9] R. Slowinski and J. Stefanowski, ”Rough classification and incomplete
information system”, Mathematiical and Computer Modeling, vol. 12,
pp. 1347-1357, 1989
[10] Z. Pawlak, and T. Munakata ,“ Rough Control, application of rough set
theory to control”, in Proc. of the Fourth European Congress on
Intelligent Techniques and Soft Computing (EUFIT’96), 1996, pp. 209-
218
[11] A. Kusiak and T. L. Tseng, “Modeling approach to data mining”, in
Proc. of the Industrial Engineering and Production Management
Conference, Glasgow, Scotland, 1999, pp. 1-13
[12] A. Kusiak, Computational Intelligence in Design and Manufacturing,
New York ,Wiley, 2000, pp. 498-527
[13] L. P. Khoo, S. B. Tor and L. Y. Zhai, “Rough set-based approach to
classification and rule induction”, İnternational Journal of Advanced
Manufacturing Technology , vol. 15, pp. 438-444, 1999
[14] Y. Xu, and C. Liu C (2013) “A rough margin-based one class support
vector machine”, Neural Computing and Applications, vol. 22, pp. 1077-
1088, 2013
[15] Z. Pawlak, RoughSets:Theoretical Aspects of Reasoning About Data ,
Boston, Kluver, vol. 9, 1992
[16] J-Y Guo and V. Chankong, V (2002) ”Rough set-based approach to rule
generation and rule induction”, International Journal of General
Systems, vol. 31, pp. 601-617, 2002
[17] J. Komorowski, L. Polkowski,and A. Skowron, “Rough Sets: A
tutorial”, In: Rough- Fuzzy Hybridization: A new method for decision
making.,Springer-Verlag, 1998
[18] T. Takagi, and M. Sugeno, “ Fuzzy identification of systems and its
application to modeling and control”, IEEE Transactions on Systems,
Man and Cybernetics, vol. 15, pp. 116-132, 1985
[19] Ş. E. Amrahov, and I. N. Askerzade, " Strong Solutions of the Fuzzy
Linear Systems", CMES - Computer Modeling in Engineering &
Sciences, vol. 76, pp. 207-216, 2011
[20] N. Gasilov, Ş. E. Amrahov, A. G. Fatullayev, H. I. Karakaş, and Ö.
Akın,"A Geometric Approach to Solve Fuzzy Linear Systems", CMES -
Computer Modeling in Engineering & Sciences, vol. 75,pp. 189-204,
2011
[21] N. Gasilov, A. G. Fatullayev and Ş. E. Amrahov, “Solution of Non-
Square Fuzzy Linear Systems”, Journal of Multiple-Valued Logic and
Soft Computing, vol. 20, pp. 221-237, 2013
[22] F. Rosenblatt, Principles of Neurodynamics: Perceptrons and the
Theory of Brain Mechanisms, Spartan Books, 1962
[23] Y. Freund and R. E. Schapire, "Large margin classification using the
perceptron algorithm", Machine Learning, vol. 37, pp. 277-296, 1999
[24] O. L. Mangasarian and W. H. Wolberg , Cancer diagnosis via linear
programming, SIAM News, vol. 23, pp 1-18, 1990
[25] D. F. Andrews and A. M. Herzberg, A Collection of Problems from
Many Fields for the Student and Research Worker, Springer, 1985
[26] J. W. Smith, J. E. Everhart, W. C. Dickson, W. C. Knowler and R. S.
Johannes, “Using the ADAP learning algorithm to forecast the onset of
diabetes mellitus”, in Proc. of the Symposium on Computer Applications
and Medical Care, pp. 261—265, 1988
[1] Z. Pawlak, “Rough Sets”, International Journal of Computer and
Information Sciences, vol. 11, pp. 341-356, 1982
[2] A. Wakulicz-Deja, A and P. Paszek, “Diagnose progressive
encephalopathy applying the rough set theory”, International Journal of
Medical Informatics, vol. 46, pp. 119-127,1997
[3] K. Slowinski, J. Stefanowski and D. Swinski, “Application of rule
induction and rough sets to verification of magnetic resonance
diagnosis”, Fundamenta Informaticae, vol. 53, pp. 345-363, 2002
[4] P. J. Lingras,“ Belief and probability based database mining”, in Proc. of
the Ninth Florida Artificial Intelligence Symposium, 1996, pp. 316-320
[5] A. Mrozek and Ekabek , “Rough sets in economic applications.” in
Rough Sets in Knowledge Discovery 2: Applications, Case Studies and
Software Systems, Polkowski and Skowron, Ed. , Germany, Verlag,
1998, pp. 238-271
[6] Z. Pawlak, ” On conflicts”, International Journal of Man-Machine
Studies, vol. 21, pp. 127-134, 1984
[7] A. Mrozek, and L. Plonka, ”Rough sets in image analysis”, Foundations
of Computing Decision Sciences, vol. 18, pp. 259-273, 1993
[8] G. Griffin and Z. Chen , “Rough set extension of Tcl for data mining”,
Knowledge-Based systems, vol. 11, pp. 249-253, 1998
[9] R. Slowinski and J. Stefanowski, ”Rough classification and incomplete
information system”, Mathematiical and Computer Modeling, vol. 12,
pp. 1347-1357, 1989
[10] Z. Pawlak, and T. Munakata ,“ Rough Control, application of rough set
theory to control”, in Proc. of the Fourth European Congress on
Intelligent Techniques and Soft Computing (EUFIT’96), 1996, pp. 209-
218
[11] A. Kusiak and T. L. Tseng, “Modeling approach to data mining”, in
Proc. of the Industrial Engineering and Production Management
Conference, Glasgow, Scotland, 1999, pp. 1-13
[12] A. Kusiak, Computational Intelligence in Design and Manufacturing,
New York ,Wiley, 2000, pp. 498-527
[13] L. P. Khoo, S. B. Tor and L. Y. Zhai, “Rough set-based approach to
classification and rule induction”, İnternational Journal of Advanced
Manufacturing Technology , vol. 15, pp. 438-444, 1999
[14] Y. Xu, and C. Liu C (2013) “A rough margin-based one class support
vector machine”, Neural Computing and Applications, vol. 22, pp. 1077-
1088, 2013
[15] Z. Pawlak, RoughSets:Theoretical Aspects of Reasoning About Data ,
Boston, Kluver, vol. 9, 1992
[16] J-Y Guo and V. Chankong, V (2002) ”Rough set-based approach to rule
generation and rule induction”, International Journal of General
Systems, vol. 31, pp. 601-617, 2002
[17] J. Komorowski, L. Polkowski,and A. Skowron, “Rough Sets: A
tutorial”, In: Rough- Fuzzy Hybridization: A new method for decision
making.,Springer-Verlag, 1998
[18] T. Takagi, and M. Sugeno, “ Fuzzy identification of systems and its
application to modeling and control”, IEEE Transactions on Systems,
Man and Cybernetics, vol. 15, pp. 116-132, 1985
[19] Ş. E. Amrahov, and I. N. Askerzade, " Strong Solutions of the Fuzzy
Linear Systems", CMES - Computer Modeling in Engineering &
Sciences, vol. 76, pp. 207-216, 2011
[20] N. Gasilov, Ş. E. Amrahov, A. G. Fatullayev, H. I. Karakaş, and Ö.
Akın,"A Geometric Approach to Solve Fuzzy Linear Systems", CMES -
Computer Modeling in Engineering & Sciences, vol. 75,pp. 189-204,
2011
[21] N. Gasilov, A. G. Fatullayev and Ş. E. Amrahov, “Solution of Non-
Square Fuzzy Linear Systems”, Journal of Multiple-Valued Logic and
Soft Computing, vol. 20, pp. 221-237, 2013
[22] F. Rosenblatt, Principles of Neurodynamics: Perceptrons and the
Theory of Brain Mechanisms, Spartan Books, 1962
[23] Y. Freund and R. E. Schapire, "Large margin classification using the
perceptron algorithm", Machine Learning, vol. 37, pp. 277-296, 1999
[24] O. L. Mangasarian and W. H. Wolberg , Cancer diagnosis via linear
programming, SIAM News, vol. 23, pp 1-18, 1990
[25] D. F. Andrews and A. M. Herzberg, A Collection of Problems from
Many Fields for the Student and Research Worker, Springer, 1985
[26] J. W. Smith, J. E. Everhart, W. C. Dickson, W. C. Knowler and R. S.
Johannes, “Using the ADAP learning algorithm to forecast the onset of
diabetes mellitus”, in Proc. of the Symposium on Computer Applications
and Medical Care, pp. 261—265, 1988
@article{"International Journal of Information, Control and Computer Sciences:69036", author = "Şahin Emrah Amrahov and Fatih Aybar and Serhat Doğan", title = "Pruning Algorithm for the Minimum Rule Reduct Generation", abstract = "In this paper we consider the rule reduct generation
problem. Rule Reduct Generation (RG) and Modified Rule
Generation (MRG) algorithms, that are used to solve this problem,
are well-known. Alternative to these algorithms, we develop Pruning
Rule Generation (PRG) algorithm. We compare the PRG algorithm
with RG and MRG.
", keywords = "Rough sets, Decision rules, Rule induction,
Classification.", volume = "9", number = "1", pages = "275-6", }
