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Volumn 29, Issue 16, 2008, Pages 2156-2164

Subgroup discover in large size data sets preprocessed using stratified instance selection for increasing the presence of minority classes

Author keywords

Instance selection; Large size data sets; Minority classes; Scaling up; Stratification; Subgroup discovery

Indexed keywords

INSTANCE SELECTION; LARGE SIZE DATA SETS; MINORITY CLASSES; SCALING UP; STRATIFICATION; SUBGROUP DISCOVERY;

EID: 53949099623     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2008.08.001     Document Type: Article
Times cited : (26)

References (37)
  • 2
    • 0025725905 scopus 로고
    • Instance-based learning algorithms
    • Aha D.W., Kibler D., and Albert M.K. Instance-based learning algorithms. Mach. Learn. 6 (1991) 37-66
    • (1991) Mach. Learn. , vol.6 , pp. 37-66
    • Aha, D.W.1    Kibler, D.2    Albert, M.K.3
  • 3
    • 33750322433 scopus 로고    scopus 로고
    • Atzmueller, M., Puppe, F., 2006. SD-Map - A fast algorithm for exhaustive subgroup discovery. In: Proc. 10th European Conf. on Principles and Practice of Knowledge Discovery in Databases (PKDD 2006), LNAI, vol. 4213, Springer-Verlag, pp. 6-17.
    • Atzmueller, M., Puppe, F., 2006. SD-Map - A fast algorithm for exhaustive subgroup discovery. In: Proc. 10th European Conf. on Principles and Practice of Knowledge Discovery in Databases (PKDD 2006), LNAI, vol. 4213, Springer-Verlag, pp. 6-17.
  • 5
    • 27144531570 scopus 로고    scopus 로고
    • A study of the behavior of several methods for balancing machine learning training data
    • Batista G.E.A.P.A., Prati R.C., and Monard M.C. A study of the behavior of several methods for balancing machine learning training data. SIGKDD Explorations 6 1 (2004) 20-29
    • (2004) SIGKDD Explorations , vol.6 , Issue.1 , pp. 20-29
    • Batista, G.E.A.P.A.1    Prati, R.C.2    Monard, M.C.3
  • 6
    • 0347763609 scopus 로고    scopus 로고
    • Using evolutionary computation as instance selection for data reduction in KDD: An experimental study
    • Cano J.-R., Herrera F., and Lozano M. Using evolutionary computation as instance selection for data reduction in KDD: An experimental study. IEEE Trans. Evol. Comput. 7 6 (2003) 561-575
    • (2003) IEEE Trans. Evol. Comput. , vol.7 , Issue.6 , pp. 561-575
    • Cano, J.-R.1    Herrera, F.2    Lozano, M.3
  • 7
    • 17444379003 scopus 로고    scopus 로고
    • Stratification for scaling up evolutionary prototype selection
    • Cano J.-R., Herrera F., and Lozano M. Stratification for scaling up evolutionary prototype selection. Pattern Recognition Lett. 26 (2005) 953-963
    • (2005) Pattern Recognition Lett. , vol.26 , pp. 953-963
    • Cano, J.-R.1    Herrera, F.2    Lozano, M.3
  • 8
    • 33947581868 scopus 로고    scopus 로고
    • Hybrid flexible neural-tree-based intrusion detection systems
    • Chen Y., Abraham A., and Yang B. Hybrid flexible neural-tree-based intrusion detection systems. Internat. J. Intell. Systems 22 (2007) 337-352
    • (2007) Internat. J. Intell. Systems , vol.22 , pp. 337-352
    • Chen, Y.1    Abraham, A.2    Yang, B.3
  • 9
    • 27144549260 scopus 로고    scopus 로고
    • Editorial: Special issue on learning from imbalanced data sets
    • Chawla N.V., Japkowicz N., and Kolcz A. Editorial: Special issue on learning from imbalanced data sets. SIGKDD Explorations 6 1 (2004) 1-6
    • (2004) SIGKDD Explorations , vol.6 , Issue.1 , pp. 1-6
    • Chawla, N.V.1    Japkowicz, N.2    Kolcz, A.3
  • 10
    • 0036102015 scopus 로고    scopus 로고
    • Adaptive sampling methods for scaling up knowledge discovery algorithms
    • Domingo C., Gavaldá R., and Watanabe O. Adaptive sampling methods for scaling up knowledge discovery algorithms. Data Mining Knowledge Discov. 6 2 (2002) 131-152
    • (2002) Data Mining Knowledge Discov. , vol.6 , Issue.2 , pp. 131-152
    • Domingo, C.1    Gavaldá, R.2    Watanabe, O.3
  • 11
    • 53949111202 scopus 로고    scopus 로고
    • Kdd'99 knowledge discovery contest
    • Elkan C. Kdd'99 knowledge discovery contest. ACM SIGKDD Explorations Newslett. 1 2 (2000) 78
    • (2000) ACM SIGKDD Explorations Newslett. , vol.1 , Issue.2 , pp. 78
    • Elkan, C.1
  • 12
    • 0001334115 scopus 로고
    • The CHC adaptive search algorithm: How to have safe search when engaging in nontraditional genetic recombination
    • Eshelman L.J. The CHC adaptive search algorithm: How to have safe search when engaging in nontraditional genetic recombination. Found. Genetic Algorithms 1 (1991) 265-283
    • (1991) Found. Genetic Algorithms , vol.1 , pp. 265-283
    • Eshelman, L.J.1
  • 14
    • 34047201415 scopus 로고    scopus 로고
    • Gao, H., Wang, X., Yang, H., 2006. LS-SVM based intrusion detection using kernel space approximation and kernel-target alignment. In: Proc. 6th World Congress on Intelligent Control and Automation, Dalian, China.
    • Gao, H., Wang, X., Yang, H., 2006. LS-SVM based intrusion detection using kernel space approximation and kernel-target alignment. In: Proc. 6th World Congress on Intelligent Control and Automation, Dalian, China.
  • 15
    • 33947251166 scopus 로고    scopus 로고
    • A hierarchical SOM-based intrusion detection system
    • Gunes H., Nur A., and Heywood M.-I. A hierarchical SOM-based intrusion detection system. Eng. Appl. Artif. Intell. 20 (2007) 439-451
    • (2007) Eng. Appl. Artif. Intell. , vol.20 , pp. 439-451
    • Gunes, H.1    Nur, A.2    Heywood, M.-I.3
  • 16
    • 0000777639 scopus 로고
    • The condensed nearest neighbour rule
    • Hart P.E. The condensed nearest neighbour rule. IEEE Trans. Inform. Theory 18 3 (1968) 431-433
    • (1968) IEEE Trans. Inform. Theory , vol.18 , Issue.3 , pp. 431-433
    • Hart, P.E.1
  • 17
    • 33747881454 scopus 로고    scopus 로고
    • APRIORI-SD: Adapting association rule learning to subgroup discovery
    • Kavšek B., and Lavrač N. APRIORI-SD: Adapting association rule learning to subgroup discovery. Appl. Artif. Intell. 20 7 (2006) 543-583
    • (2006) Appl. Artif. Intell. , vol.20 , Issue.7 , pp. 543-583
    • Kavšek, B.1    Lavrač, N.2
  • 18
    • 53949083571 scopus 로고    scopus 로고
    • Kavšek, B., Lavrač, N., Bullas, J.C., 2002. Rule induction for subgroup discovery: A case study in mining UK traffic accident data. In: Proc. Internat. Multi-Conf. on Information Society (IS'02), pp. 127-130.
    • Kavšek, B., Lavrač, N., Bullas, J.C., 2002. Rule induction for subgroup discovery: A case study in mining UK traffic accident data. In: Proc. Internat. Multi-Conf. on Information Society (IS'02), pp. 127-130.
  • 19
    • 34248215596 scopus 로고    scopus 로고
    • Introducing a very large dataset of handwritten Farsi digits and a study on their varieties
    • Khosravi H., and Kabir E. Introducing a very large dataset of handwritten Farsi digits and a study on their varieties. Pattern Recognition Lett. 28 10 (2007) 1133-1141
    • (2007) Pattern Recognition Lett. , vol.28 , Issue.10 , pp. 1133-1141
    • Khosravi, H.1    Kabir, E.2
  • 20
    • 30944444787 scopus 로고    scopus 로고
    • Artificial neural networks with evolutionary instance selection for financial forecasting
    • Kim K. Artificial neural networks with evolutionary instance selection for financial forecasting. Expert Systems Appl. 30 3 (2006) 519-526
    • (2006) Expert Systems Appl. , vol.30 , Issue.3 , pp. 519-526
    • Kim, K.1
  • 21
    • 0002192370 scopus 로고    scopus 로고
    • Explora: A multipattern and multiestrategy discovery assistant
    • MIT Press
    • Klöesgen W. Explora: A multipattern and multiestrategy discovery assistant. Advances in Knowledge Discovery and Data Mining (1996), MIT Press 249-271
    • (1996) Advances in Knowledge Discovery and Data Mining , pp. 249-271
    • Klöesgen, W.1
  • 22
    • 53949111205 scopus 로고    scopus 로고
    • Klöesgen, W., Michael, M., 2002. Census data mining - An application. In: Proc. 5th European Conf. on Principles of Data Mining, Knowledge Discovery (PKDD'02), pp. 65-79.
    • Klöesgen, W., Michael, M., 2002. Census data mining - An application. In: Proc. 5th European Conf. on Principles of Data Mining, Knowledge Discovery (PKDD'02), pp. 65-79.
  • 23
    • 0000935031 scopus 로고
    • Editing for the k-nearest neighbours rule by a genetic algorithm
    • Kuncheva L. Editing for the k-nearest neighbours rule by a genetic algorithm. Pattern Recognition Lett. 16 (1995) 809-814
    • (1995) Pattern Recognition Lett. , vol.16 , pp. 809-814
    • Kuncheva, L.1
  • 24
    • 84949207526 scopus 로고    scopus 로고
    • Lavrač, N., Flach, P., Zupan, B., 1999. Rule evaluation measures: A unifying view. In: Proc. 9th Internat. Workshop on Inductive Logic Programming, pp. 174-185.
    • Lavrač, N., Flach, P., Zupan, B., 1999. Rule evaluation measures: A unifying view. In: Proc. 9th Internat. Workshop on Inductive Logic Programming, pp. 174-185.
  • 25
    • 0038433993 scopus 로고    scopus 로고
    • Lavrač, N., Flach, P., Kavšek, B., Todorovski, L., 2002. Adapting classification rule induction to subgroup discovery. In: Proc. IEEE Internat. Conf. on Data Mining, pp. 266-273.
    • Lavrač, N., Flach, P., Kavšek, B., Todorovski, L., 2002. Adapting classification rule induction to subgroup discovery. In: Proc. IEEE Internat. Conf. on Data Mining, pp. 266-273.
  • 26
    • 3242791702 scopus 로고    scopus 로고
    • Decision support through subgroup discovery: Three case studies and the lessons learned
    • Lavrač N., Cestnik B., Gamberger D., and Flach P. Decision support through subgroup discovery: Three case studies and the lessons learned. Machine Learn. 57 (2004) 115-143
    • (2004) Machine Learn. , vol.57 , pp. 115-143
    • Lavrač, N.1    Cestnik, B.2    Gamberger, D.3    Flach, P.4
  • 29
    • 53949110356 scopus 로고    scopus 로고
    • Newman, D.J., Hettich, S., Blake, C.L., Merz, C.J., 1998. UCI Repository of machine learning databases. University of California, Irvine, Dept. of Information and Computer Sciences. .
    • Newman, D.J., Hettich, S., Blake, C.L., Merz, C.J., 1998. UCI Repository of machine learning databases. University of California, Irvine, Dept. of Information and Computer Sciences. .
  • 30
    • 0036555992 scopus 로고    scopus 로고
    • Finding representative patterns with ordered projections
    • Riquelme J.C., Aguilar J.S., and Toro M. Finding representative patterns with ordered projections. Pattern Recognition 36 (2003) 1009-1018
    • (2003) Pattern Recognition , vol.36 , pp. 1009-1018
    • Riquelme, J.C.1    Aguilar, J.S.2    Toro, M.3
  • 31
    • 84908362061 scopus 로고    scopus 로고
    • Todorovski, L., Flach, P., Lavrač, N., 2000. Predictive performance of weighted relative accuracy. In: Proc. 4th European Conf. on Principles of Data Mining and Knowledge Discovery, pp. 255-264.
    • Todorovski, L., Flach, P., Lavrač, N., 2000. Predictive performance of weighted relative accuracy. In: Proc. 4th European Conf. on Principles of Data Mining and Knowledge Discovery, pp. 255-264.
  • 32
    • 33747892727 scopus 로고    scopus 로고
    • Partition based pattern synthesis technique with efficient algorithms for nearest neighbor classification
    • Viswanath P., Narasimba M., and Bhatnagar S. Partition based pattern synthesis technique with efficient algorithms for nearest neighbor classification. Pattern Recognition Lett. 27 14 (2006) 1714-1724
    • (2006) Pattern Recognition Lett. , vol.27 , Issue.14 , pp. 1714-1724
    • Viswanath, P.1    Narasimba, M.2    Bhatnagar, S.3
  • 33
    • 0343081513 scopus 로고    scopus 로고
    • Reduction techniques for instance-based learning algorithms
    • Wilson D.R., and Martinez T.R. Reduction techniques for instance-based learning algorithms. Machine Learn. 38 (2000) 257-268
    • (2000) Machine Learn. , vol.38 , pp. 257-268
    • Wilson, D.R.1    Martinez, T.R.2
  • 35
    • 33751355996 scopus 로고    scopus 로고
    • Yen, S.J., Lee, Y.S., 2006. Cluster-based sampling approaches to imbalanced data distributions. In: Proc. Data Warehousing and Knowledge Discovery (DaWak 2006), Lecture Notes in Computer Science, vol. 4081, Springer-Verlag, pp. 427-436.
    • Yen, S.J., Lee, Y.S., 2006. Cluster-based sampling approaches to imbalanced data distributions. In: Proc. Data Warehousing and Knowledge Discovery (DaWak 2006), Lecture Notes in Computer Science, vol. 4081, Springer-Verlag, pp. 427-436.
  • 37
    • 53949089896 scopus 로고    scopus 로고
    • Zhang, J., Mani, I., 2003. kNN approach to unbalanced data distributions: A case study involving information extraction. In: Proc. ICML'2003 Workshop on Learning from Imbalanced Datasets, 2003.
    • Zhang, J., Mani, I., 2003. kNN approach to unbalanced data distributions: A case study involving information extraction. In: Proc. ICML'2003 Workshop on Learning from Imbalanced Datasets, 2003.


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