Top

Knowledge and Information Systems

Published in:

06-10-2017 | Regular Paper

A hybrid approach for classification of rare class data

Authors: Kapil Keshao Wankhade, Kalpana C. Jondhale, Vijaya R. Thool

Published in: Knowledge and Information Systems | Issue 1/2018

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Learning of rare class data is a challenging problem in field of classification process. A rare class or imbalanced class learning is the common problem faced by many real-world applications, because of this many researcher work focused on this issue. Rare class data always generate wrong results because of overwhelming accuracy of minority class by majority class. There are lots of methods been proposed to handle imbalanced class or rare class or skew class problem. This paper proposes a hybrid method, i. e. classification- and clustering-based method, solving rare class problem. This proposed hybrid method uses k-means, ensemble and divide and merge methods. This method tries to improve detection rate of every class. For experimental work, the proposed method is tested on real datasets. The experimental results show that proposed method works well as compared with other algorithms.

previous article Exploiting highly qualified pattern with frequency and weight occupancy

next article OntoILPER: an ontology- and inductive logic programming-based system to extract entities and relations from text

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Gudadhe M, Prakash P, Wankhade K (2010) A new data mining based network intrusion detection model. In: The proceedings of international conference on computer and communication technology (IEEE), Allahabad, India, pp 731–735

Medioni G, Cohen I, Brémond F, Hongeng S, Nevatia R (2001) Event detection and analysis from video streams. IEEE Trans Pattern Anal Mach Intell 2001 23(8):873–889CrossRef

Zhong H, Shi J, Visontai M (2004) Detecting unusual activity in video. In: The proceeding of the IEEE computer society conference on computer vision and pattern recognition (CVPR’04), 2004, Washington, DC, 2:819–826

Huda S, Yearwood J, Jelinek HF, Hassan MM, Fortino G, Buckland M (2016) A hybrid feature selection with ensemble classification for imbalanced healthcare data: a case study for brain tumor diagnosis. IEEE Access 4:9145–9154CrossRef

Lewis DD, Gale WA (1994) A sequential algorithm for training text classifiers. In: The proceedings of 17th annual international ACM SIGIR conference on research and development in information retrieval (SIGIR’94), Dublin, Ireland, pp 3–12

Kubat M, Holte RC, Matwin S (1998) Machine learning for the detection of oil spills in satellite radar images. J Mach Learn 30(2):195–215CrossRef

Phua C, Alahakoon D, Lee V (2004) Minority report in fraud detection: classification of skewed data. ACM SIGKDD Explor Newslett 6(1):50–59 (Special Issue on Learning from Imbalanced Datasets)CrossRef

Sit WY, Mao KZ (2013) Learning imbalanced classes in the presence of concept growth. In: The proceeding of IEEE conference on evolving and adaptive intelligent systems (EAIS), 2013, pp 62–69

Lin SC, Chang CYI, Yang WN (2009) Meta-learning for imbalanced data and classification ensemble in binary classification. J Neurocomput 73(1–3):484–494CrossRef

10.

Khoshgoftaar TM, Seiffert C, Hulse JV, Napolitano A, Folleco A (2007) Learning with limited minority class data. In: The proceeding of 6th international conference on machine learning and applications (IEEE), pp 348–353

11.

Wang S, Yao X (2013) Relationships between diversity of classification ensembles and single-class performance measures. IEEE Trans Knowl Data Eng 25(1):206–219CrossRef

12.

He H, Garcia EA (2009) Learning from imbalanced data. IEEE Trans Knowl Data Eng 21(9):1263–1284CrossRef

13.

Seiffert C, Khoshgoftaar TM, Hulse JV, Napolitano A (2007) Mining data with rare events: a case study. In: The proceeding of the 19th IEEE international conference on tools with artificial intelligence, pp 132–139

14.

Galar M, Fernandez A, Barrenechea E, Bustince H, Herrera F (2011) A review on ensembles for the class imbalance problem: bagging–boosting-, and hybrid-based approaches. IEEE Trans Syst Man Cybern-Part C: Appl Rev 42(4):463–484CrossRef

15.

Krawczyk B, Schaefer G, Wozniak M (2013) An evaluation of classifier ensembles for class imbalance problems. In: The proceeding of international conference on informatics, electronics and vision (ICIEV-IEEE), pp 1–4

16.

Wang S, Yao X (2012) Multiclass imbalance problems: analysis and potential solutions. IEEE Trans Syst Man Cybern-Part B: Cybern 42(4):1119–1130CrossRef

17.

Liu N, Woon WL, Aung Z, Afshari A (2014) Handling class imbalance in customer behavior prediction. In: The proceedings of international conference on collaboration technologies and systems (CTS-IEEE), pp 100–103

18.

Yang Z, Tang W, Shintemirov A, Wu Q (2009) Association rule mining-based dissolved gas analysis for fault diagnosis of power transformers. IEEE Trans Syst Man Cybern—Part C Appl Rev 39(6):597–610CrossRef

19.

Zhu ZB, Song ZH (2010) Fault diagnosis based on imbalance modified kernel fisher discriminant analysis. J Chem Eng Res Des 88(8):936–951CrossRef

20.

Khreich W, Granger E, Miri A, Sabourin R (2010) Iterative boolean combination of classifiers in the roc space: an application to anomaly detection with HMMs. J Pattern Recognit 43(8):2732–2752CrossRefMATH

21.

Tavallaee M, Stakhanova N, Ghorbani A (2010) Toward credible evaluation of anomaly-based intrusion-detection methods. IEEE Trans Syst Cybern: Part C Appl Rev 40(5):516–524

22.

Mazurowski MA, Habas PA, Zurada JM, Lo JY, Baker JA, Tourassi GD (2008) Training neural network classifiers for medical decision making: the effects of imbalanced datasets on classification performance. Neural Netw 21(2–3):427–436CrossRef

23.

del Castillo MD, Serrano JI (2004) A multi strategy approach for digital text categorization from imbalanced documents. ACM SIGKDD Explor Newslett 6(1):70–79 (Special Issue on Learning from Imbalanced Datasets)CrossRef

24.

Turney PD (2000) Learning algorithms for key phrase extraction. J Inf Retr 2(4):303–336CrossRef

25.

Ling CX, Li C (1998) Data mining for direct marketing: problems and solutions. In: The proceedings of 4th international conference on knowledge discovery and data mining (KDD), pp 73–79

26.

Bermejo P, Gamez JA, Puerta JM (2011) Improving the performance of naive bayes multinomial in e-mail foldering by introducing distribution based balance of datasets. J Expert Syst Appl 38(3):2072–2080CrossRef

27.

Liu YH, Chen YT (2005) Total margin-based adaptive fuzzy support vector machines for multiview face recognition. In: The proceeding IEEE international conference on system, man and cybernetics 2:1704–1711

28.

Breiman L (1996) Bagging predictors. J Mach Learn 24(2):123–140MATH

29.

Freund Y, Schapire RE (1997) A decision-theoretic generalization of online learning and an application to boosting. J Comput Syst Sci 55(1):119–139CrossRefMATH

30.

Lin S, Wang C, Wu Z, Chung Y (2013) Detect rare events via MICE algorithm with optimal threshold. In: The proceeding of 7th international conference on innovative mobile and internet services in ubiquitous computing (IEEE), pp 70–75

31.

Seiffert C, Khoshgoftaar TM, Hulse JV, Napolitano A (2010) RUSBoost: a hybrid approach to alleviating class imbalance. IEEE Trans Syst Man Cybern-Part A: Syst Hum 40(1):185–197CrossRef

32.

Oh S, Lee MS, Zhang B (2011) Ensemble learning with active example selection for imbalanced biomedical data classification. IEEE/ACM Trans Comput Biol Bioinf 8(2):316–325CrossRef

33.

Yang P, Yoo PD, Fernando J, Zhou BB, Zhang Z, Zomaya AY (2014) Sample subset optimization techniques for imbalanced and ensemble learning problems in bioinformatics applications. IEEE Trans Cybern 44(3):445–455CrossRef

34.

Ditzler G, Polikar R (2013) Incremental learning of concept drift from streaming imbalanced data. IEEE Trans Knowl Data Eng 25(10):2283–2301CrossRef

35.

Sun Y, Kamel MS, Wang Y (2006) Boosting for learning multiple classes with imbalanced class distribution. In: The proceeding of 6th international conference on data mining (ICDM), pp 592–602

36.

Krawczyk B, Schaefer G, Wozniak M (2013) Combining one-class classifiers for imbalanced classification of breast thermogram features. In: The proceeding of the IEEE 4th international workshop on computational intelligence in medical imaging (CIMI), 2013, pp 36–41

37.

Wang S, Minku LL, Yao X (2015) Resampling-based ensemble methods for online class imbalance learning. IEEE Trans Knowl Data Eng 27(5):1356–1368CrossRef

38.

Ahumada H, Grinblat GL, Uzal LC, Granitto PM, Ceccatto A (2008) REPMAC: A new hybrid approach to highly imbalanced classification problems. In: The proceeding of 8th international conference on hybrid intelligent systems (IEEE) pp 386–391

39.

Jeatrakul P, Wong KW (2012) Enhancing classification performance of multi-class imbalanced data using the OAA-DB algorithm. In: The proceeding of IEEE world congress on computational intelligence (WCCI), pp 1–8

40.

Tan SC, Watada J, Ibrahim Z, Khalid M, Jau LW, Chew LC (2011), Learning with imbalanced datasets using fuzzy ARTMAP-based neural network models. In: The proceeding of IEEE international conference on fuzzy systems, 2011, Taiwan, pp 1084–1089

41.

Cao P, Li B, Zhao D, Zaiane O (2013) A novel cost sensitive neural network ensemble for multiclass imbalance data learning. In: The proceeding of international joint conference on neural networks (IJCNN- IEEE) pp 1–8

42.

Fu J, Lee S (2011) Certainty-enhanced active learning for improving imbalanced data classification. In: The proceeding of 11th IEEE international conference on data mining workshops, IEEE, pp 405–412

43.

Antwi DK, Viktor HL, Japkowicz N (2012) The PerfSim algorithm for concept drift detection in imbalanced data. In: The proceeding of 12th IEEE international conference on data mining workshops, pp 619–628

44.

Alhammady H, Ramamohanarao K (2004) Using emerging patterns and decision trees in rare-class classification. In: The proceedings of the 4th IEEE international conference on data mining (ICDM’04), pp 315–318

45.

Wang P, Wang H, Wu X, Wang W, Shi B (2007) A low-granularity classifier for data streams with concept drifts and biased class distribution. IEEE Trans Knowl Data Eng 19(9):1202–1213CrossRef

46.

Thach NH, Rojanavasu P, Pinngern O (2008) Cost-sensitive XCS classifier system addressing imbalance problems. In: The proceeding of 5th international conference on fuzzy systems and knowledge discovery, pp 132–136

47.

Orriols-Puig A, Bernadó-Mansilla E, Goldberg DE, Sastry K, Lanzi PL (2009) Facetwise analysis of XCS for problems with class imbalances. IEEE Trans Evol Comput 13(5):1093–1119CrossRef

48.

He J, Tong H, Carbonell J (2010) Rare category characterization. In: The proceeding of IEEE international conference on data mining, pp 226–235

49.

Wallace BC, Dahabreh IJ (2012) Class probability estimates are unreliable for imbalanced data (and how to fix them). In: The proceeding of IEEE 12th international conference on data mining, pp 695–704

50.

Hospedales TM, Gong S, Xiang T (2013) Finding rare classes: active learning with generative and discriminative models. IEEE Trans Knowl Data Eng 25(2):374–386CrossRef

51.

Own HS, AAl NAA, Abraham A (2010) A new weighted rough set framework for imbalance class distribution. In: The proceeding of international conference of soft computing and pattern recognition (IEEE), pp 29–34

52.

Huang K, Yang H, King I, Lyu MR (2006) Imbalanced learning with a biased minimax probability machine. IEEE Trans Syst Man Cybern-Part B: Cybern 36(4):913–923CrossRef

53.

Huang K, Yang H, King I, Lyu MR (2004) Learning classifiers from imbalanced data based on biased minimax probability machine. In: The proceeding of the IEEE computer society conference on computer vision and pattern recognition (CVPR’04), 2004, pp 558–563

54.

Su C, Hsiao Y (2007) An evaluation of the robustness of MTS for imbalanced data. IEEE Trans Knowl Data Eng 19(10):1321–1332CrossRef

55.

Diamantini C, Potena D (2009) Bayes vector quantizer for class-imbalance problem. IEEE Trans Knowl Data Eng 21(5):638–651CrossRef

56.

Williams DP, Myers V, Silvious MS (2009) Mine classification with imbalanced data. IEEE Geosci Remote Sens Lett 6(3):528–532CrossRef

57.

Castro CL, Braga AP (2013) Novel cost-sensitive approach to improve the multilayer perceptron performance on imbalanced data. IEEE Trans Neural Netw Learn Syst 24(6):888–899CrossRef

58.

Wu G, Chang EY (2005) KBA: kernel boundary alignment considering imbalanced data distribution. IEEE Trans Knowl Data Eng 17(6):786–795CrossRef

59.

Chen S, He H (2009) SERA: selectively recursive approach towards nonstationary imbalanced stream data mining. In: The proceeding of international joint conference on neural networks (IEEE) USA, pp 522–529

60.

Fu J, Lee S (2011) Certainty-enhanced active learning for improving imbalanced data classification. In: The proceeding of the 11th IEEE international conference on data mining workshops, 2011, pp 405–412

61.

Yang Z, Gao D (2012) An active under-sampling approach for imbalanced data classification. In: The proceeding of the 5th international symposium on computational intelligence and design (IEEE), pp 270–273

62.

Kwak J, Lee T, Kim CO (2015) An incremental clustering-based fault detection algorithm for class-imbalanced process data. IEEE Trans Semicond Manuf 28(3):1–11CrossRef

63.

Zhang X, Hu B (2014) A new strategy of cost-free learning in the class imbalance problem. IEEE Trans Knowl Data Eng 26(12):2872–2885CrossRef

64.

Park S, Ha Y (2014) Large imbalance data classification based on mapreduce for traffic accident prediction. In: The proceeding of 8th international conference on innovative mobile and internet services in ubiquitous computing, pp. 45–49

65.

Das B, Krishnan NC, Cook DJ (2015) RACOG and wRACOG: two probabilistic oversampling techniques. IEEE Trans Knowl Data Eng 27(1):222–234CrossRef

66.

Yu X, Zhang X (2012) Imbalanced data classification algorithm based on hybrid model. In: The proceeding of international conference on machine learning and cybernetics (IEEE) pp 735–740

67.

Tang Y, Zhang Y, Chawla NV, Krasser S (2009) SVMs modeling for highly imbalanced classification. IEEE Trans Syst Man and Cybern-Part B: Cybern 39(1):281–288CrossRef

68.

Phoungphol P, Zhang Y, Zhao Y, Srichandan B (2012) Multiclass SVM with ramp loss for imbalanced data classification. In: The proceeding of the IEEE international conference on granular computing, 2012, pp 376–381

69.

Zhou X, Lu S, Hu L, Zhang M (2012) Imbalanced extreme support vector machine. In: The proceeding of the international conference on machine learning and cybernetics (IEEE), 2012, pp 483–489

70.

Anand R, Mehrotra KG, Mohan KC, Ranka S (1993) An improved algorithm for neural network classification of imbalanced training sets. IEEE Trans Neural Netw 4(6):962–969CrossRef

71.

Lin M, Tang K, Yao X (2013) Dynamic sampling approach to training neural networks for multiclass imbalance classification. IEEE Trans Neural Netw Learn Syst 24(4):647–660CrossRef

72.

Vorraboot P, Rasmequan S, Lursinsap C, Chinnasarn K (2012) A modified error function for Imbalanced dataset classification problem. In: The proceeding of 7th international conference on computing and convergence technology (ICCCT-IEEE), pp 854–859

73.

Lee MS, Oh S, Zhang B (2009) Ensemble learning based on active example selection for solving imbalanced data problem in biomedical data. In: The proceeding of IEEE international conference on bioinformatics and biomedicine, pp 350–355

74.

Murphey YL, Wang H, Ou G, Feldkamp LA (2007), OAHO: an effective algorithm for multi-class learning from imbalanced data. In: The proceeding of international joint conference on neural networks (IEEE) USA, pp 406–411

75.

Nguyen HM, Cooper EW, Kamei K (2011) Online learning from imbalanced data streams. In: The proceeding of international conference of soft computing and pattern recognition (SoCPaR-IEEE), pp 347–352

76.

Koknar-Tezel S, Latecki LJ (2009) Improving SVM classification on imbalanced data sets in distance spaces. In: The proceeding of 9th IEEE international conference on data mining, pp 259–267

77.

Zhou B, Yang C, Guo H, Hu J (2013) A Quasi-linear SVM combined with assembled SMOTE for imbalanced data classification. In: The proceeding of international joint conference on neural networks (IJCNN-IEEE), 2013, pp 1–7

78.

Pengfei J, Chunkai Z, Zhenyu H (2014) A new sampling approach for classification of imbalanced data sets with high density. In: The proceeding of international conference on big data and smart computing (BigComp-IEEE) pp 217–222

79.

Huang H, Lin Y, Chen Y, Lu H (2012) Imbalanced data classification using random subspace method and SMOTE. In: The proceeding of joint 6th international conference on soft computing and intelligent systems (SCIS) and 13th international symposium on advanced intelligent systems (ISIS), 2012, Japan, pp 817–820

80.

Rashu RI, Haq N, Rahman RM (2014) Data mining approaches to predict final grade by overcoming class imbalance problem. In: The proceeding of 17th international conference on computer and information technology (ICCIT), pp 14–19

81.

Han J, Kamber M (2006) Data Mining : Concepts and Techniques, 2nd edn. Morgan Kaufmann Publishers, BurlingtonMATH

82.

Muda Z, Yassin W, Sulaiman MN, Udzir NI (2011) Intrusion detection based on K-means clustering and Naïve Bayes classification. In: Proceedings of 7th International Conference on IT in Asia (CITA-IEEE) pp 1–6

83.

Attar V, Sinha P, Wankhade K (2010) A fast and light classifier for data streams. Spring Evolv Syst 1(3):199–207CrossRef

84.

Cheng D, Kannan R, Vempala S, Wang G (2006) A divide-and-merge methodology for clustering. ACM Trans Database Syst 21(4):1499–1525CrossRef

85.

UCI machine learning repository. http://archive.ics.uci.edu/ml/datasets.html

86.

Oza N, Russell S (2001) Online bagging and boosting. In: Artificial intelligence and statistics, Morgan Kaufmann, pp 105–112

87.

Pelossof R, Jones M, Vovsha I, Rudin C (2008) Online coordinate boosting, pp 1–9. arXiv:0810.4553

88.

Bieft A, Holmes G, Pfahringer B, Kirkby R, Gavalda R (2009) New ensemble methods for evolving data streams. In: KDD, pp 139–148

89.

Witten I, Frank E (2005) Data mining: practical machine learning tools and techniques, Morgan Kaufmann series in data management systems, 2nd ed, pp 1–525

Title: A hybrid approach for classification of rare class data
Authors: Kapil Keshao Wankhade
Kalpana C. Jondhale
Vijaya R. Thool
Publication date: 06-10-2017
Publisher: Springer London
Published in: Knowledge and Information Systems / Issue 1/2018
Print ISSN: 0219-1377
Electronic ISSN: 0219-3116
DOI: https://doi.org/10.1007/s10115-017-1114-5

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Other articles of this Issue 1/2018

Estimating effectiveness of twitter messages with a personalized machine learning approach

OntoILPER: an ontology- and inductive logic programming-based system to extract entities and relations from text

Automatic feature selection for supervised learning in link prediction applications: a comparative study

Mining exceptional closed patterns in attributed graphs

Dynamic affinity-based classification of multi-class imbalanced data with one-versus-one decomposition: a fuzzy rough set approach

Tractable queries on big data via preprocessing with logarithmic-size output

Premium Partner