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Knowledge and Information Systems
Erschienen in: Knowledge and Information Systems 3/2017

27.05.2016 | Regular Paper

DBMUTE: density-based majority under-sampling technique

verfasst von: Chumphol Bunkhumpornpat, Krung Sinapiromsaran

Erschienen in: Knowledge and Information Systems | Ausgabe 3/2017

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Abstract

Class imbalance is a challenging problem that demonstrates the unsatisfactory classification performance of a minority class. A trivial classifier is biased toward minority instances because of their tiny fraction. In this paper, our density function is defined as the distance along the shortest path between each majority instance and a minority-cluster pseudo-centroid in an underlying cluster graph. A short path implies highly overlapping dense minority instances. In contrast, a long path indicates a sparsity of instances. A new under-sampling algorithm is proposed to eliminate majority instances with low distances because these instances are insignificant and obscure the classification boundary in the overlapping region. The results show predictive improvements on a minority class from various classifiers on different UCI datasets.
Vorheriger Artikel Toward value difference metric with attribute weighting
Nächster Artikel DASC: data aware algorithm for scalable clustering

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Literatur
1.
2.
Bradley AP (1997) The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognit 30(6):1145–1159CrossRef
3.
Buckland M, Gey F (1994) The relationship between recall and precision. J Am Soc Inf Sci 45(1):12–19CrossRef
4.
Bunkhumpornpat C, Sinapiromsaran K, Lursinsap C (2012) DBSMOTE: density-based synthetic minority over-sampling technique. Appl. Intell. 36(3):664–684CrossRef
5.
Bunkhumpornpat C, Sinapiromsaran, K, Lursinsap C (2011) MUTE: majority under-sampling technique. In: The 8th international conference on information, communications, and signal processing, Singapore
6.
Bunkhumpornpat C, Sinapiromsaran K, Lursinsap C (2009) Safe-level-SMOTE: Safe-level-synthetic minority over-sampling technique for handling the class imbalanced problem. In: Theeramunkong T, Kijsirikul B, Cercone N, Ho T-B (eds) The 13th Pacific-Asia conference on knowledge discovery and data Mining, Bangkok, Thailand, vol 5476., Lecture notes in artificial intelligence. Springer, Heidelberg, pp 475–482
7.
Chawla NV (2010) Data mining for imbalanced datasets: an overview. In: Maimon O, Rokach L (eds) Data mining and knowledge discovery handbook. Springer, Berlin, pp 875–886
8.
Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16:341–378MATH
9.
Chiang I-J, Shieh M-J, Hsu JY, Wong J-M (2005) Building a medical decision support system for colon polyp screening by using fuzzy classification trees. Special issue: foundations and advances in data mining. Appl Intell 22(1):61–75CrossRef
10.
Cohen WW (1995) Fast effective rule induction. In: 12th international conference on machine learning. Lake Tahoe, CA, pp 115–123
11.
Cover T, Hart PE (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theory 13(1):21–27CrossRefMATH
12.
Demsar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30MathSciNetMATH
13.
Drummond C, Holte RC (2003) C4.5, Class imbalance, and cost sensitivity: why under-sampling beats over-sampling. In: the ICML 2003 workshop on learning from imbalanced datasets II, Washington, DC, pp 1–8
14.
Ester M, Kriegel H-P, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: The 2nd international conference on knowledge discovery and data mining. Portland, Oregon, USA, pp 226–231
15.
Frank E, Bouckaert RR (2006) Naive Bayes for text classification with unbalanced classes. In: The 10th European conference on principles and practice of knowledge discovery in databases, Berlin, Germany, pp 503–510
16.
Garcia V, Sánchez JS, Mollineda RA, Alejo R, Sotoca JM (2007) The class imbalance problem in pattern classification and learning. IV Taller Nacional de Minería de Datos y Aprendizaje (TAMIDA 2007). Zaragoza, Spain, pp 283–291
17.
Han H, Wang W-Y, Mao B-H (2005) Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning. In: Huang D-S, Zhang X-P, Huang G-B (eds) The 2005 international conference on intelligent computing, Hefei, China. Lecture notes in computer science, vol 3644. Springer, Heidelberg, pp 878–887
18.
Han J, Kamber M, Pei J (2011) Data mining: concepts and techniques, 3rd edn. Morgan Kaufmann, BurlingtonMATH
19.
Hu X (2005) A data mining approach for retailing bank customer attrition analysis. Special issue: foundations and advances in data mining. Appl Intell 22(1):47–60CrossRef
20.
Japkowicz N (2000) The class imbalance problem: significance and strategies. In: The 2000 international conference on artificial intelligence, Las Vegas, NV, pp 111–117
21.
Japkowicz N, Stephen S (2002) The class imbalance problem: a systematic study. Intell Data Anal 6(5):429–449MATH
22.
Khor K-C, Ting C-Y, Phon-Amnuaisuk S (2012) A cascaded classifier approach for improving detection rates on rare attack categories in network intrusion detection. Appl Intell 36(2):320–329CrossRef
23.
Kubat M, Holte R, Matwin S (1998) Machine learning for the detection of oil spills in satellite radar images. Mach Learn 30(2–3):195–215CrossRef
24.
Murphey YL, Chen ZH, Feldkamp LA (2008) An incremental neural learning framework and its application to vehicle diagnostics. Appl Intell 28(1):29–49CrossRef
25.
Murty MN, Devi VS (2012) Pattern recognition: an algorithmic approach. Springer, BerlinMATH
26.
Nguyen GH, Bouzerdoum A, Phung SL (2009) Learning Pattern Classification Tasks with Imbalanced Data Sets. In: Yin P (ed) Pattern recognition. In-Teh, Vukovar, pp 193–208
27.
Prati RC, Batista GEAPA, Monard MC (2004) Class imbalances versus class overlapping: an analysis of a learning system behavior. In: Monroy R, Arroyo G, Sucar LE, Sossa H (eds.) The 3rd Mexican international conference on artificial intelligence, Mexico City, Mexico. Lecture Notes in artificial intelligence, vol 2972, pp 312–321
28.
Quinlan JR (1992) C4.5: programs for machine learning. Morgan Kaufmann, Burlington
29.
Tetko IV, Livingstone DJ, Luik AI (1995) Neural network studies. 1. Comparison of overfitting and overtraining. J Chem Inf Comput Sci 35(5):826–833CrossRef
30.
31.
Wang S, Li Z, Chao W-H, Cao Q (2012) Applying adaptive over-sampling technique based on data density and cost-sensitive SVM to imbalanced learning. In: IJCNN, pp 1–8
32.
Witten IH, Frank E, Hall MA (2011) Data mining: practical machine learning tools and techniques, 3rd edn. Morgan Kaufmann, Burlington
Metadaten
Titel
DBMUTE: density-based majority under-sampling technique
verfasst von
Chumphol Bunkhumpornpat
Krung Sinapiromsaran
Publikationsdatum
27.05.2016
Verlag
Springer London
Erschienen in
Knowledge and Information Systems / Ausgabe 3/2017
Print ISSN: 0219-1377
Elektronische ISSN: 0219-3116
DOI
https://doi.org/10.1007/s10115-016-0957-5

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