The most critical issue in real world applications are class imbalance problems. Imbalanced data sets are common across different domain including banking, health care, finance and other. When such data sets are trained on typical classification algorithm they tends to be biased towards the majority class. The learning task becomes more challenging when there is also an overlap of instances from different classes. In this paper, we propose an undersampling framework for binary classification datasets by removing overlapped data points called Critical Instances Removal based Under-Sampling (CIRUS). Our method is designed to identify and eliminate majority class instances from the overlapping region. Accurate identification and elimination of these instances maximise the visibility of the minority class instances and at the same time minimises excessive elimination of data, which reduces loss of information. Extensive experiments using simulated and real-world datasets were carried out and the results show comparable performance with state-of-the-art methods across different common metrics with exceptional and statistically significant improvements in sensitivity.
Alcalá-FdezJ.FernándezA.LuengoJ.DerracJ.GarcíaS.SánchezL. and HerreraF., Keel data-mining software tool: data set repository, integration of algorithms and experimental analysis framework, Journal of Multiple-Valued Logic & Soft Computing, 2011, 17.
2.
AşkanA. and SayınS., Svm classification for imbalanced data sets using a multiobjective optimization framework, Annals of Operations Research216(1) (2014), 191–203.
3.
BaruaS.IslamM.M.YaoX. and MuraseK., Mwmote-majority weighted minority oversampling technique for imbalanced data set learning, IEEE Transactions on Knowledge and Data Engineering26(2) (2012), 405–425.
4.
BunkhumpornpatC. and SinapiromsaranK., Dbmute: density-based majority under-sampling technique, Knowledge and Information Systems50(3) (2017), 827–850.
5.
BunkhumpornpatC.SinapiromsaranK. and LursinsapC., Safe-level-smote: Safe-level-synthetic minority over-sampling technique for handling the class imbalanced problem, in: Pacific-asia Conference on Knowledge Discovery and Data Mining, Springer, 2009, pp. 475–482.
DasS.DattaS. and ChaudhuriB.B., Handling data irregularities in classification: foundations, trends, and future challenges, Pattern Recognition81 (2018), 674–693.
11.
DeviD.PurkayasthaB. et al., Redundancy-driven modified tomek-link based undersampling: a solution to class imbalance, Pattern Recognition Letters93 (2017), 3–12.
12.
DouzasG.BacaoF. and LastF., Improving imbalanced learning through a heuristic oversampling method based on k-means and smote, Information Sciences465 (2018), 1–20.
13.
EzawaK.J.SinghM. and NortonS.W., Learning goal oriented bayesian networks for telecommunications risk management, in: Proceedings of the International Conference on Machine Learning, 1996, pp. 139–147.
14.
FawcettT. and ProvostF.J., Combining data mining and machine learning for effective user profiling, in: Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, 1996, pp. 8–13.
15.
FernáNdezA.LóPezV.GalarM.Del JesusM.J. and HerreraF., Analysing the classification of imbalanced data-sets with multiple classes: binarization techniques and ad-hoc approaches, Knowledge-based Systems42 (2013), 97–110.
16.
FreitasA.Costa-PereiraA. and BrazdilP., Cost-sensitive decision trees applied to medical data, in: International Conference on Data Warehousing and Knowledge Discovery, Springer, 2007, pp. 303–312.
17.
GarcíaS. and HerreraF., Evolutionary undersampling for classification with imbalanced datasets: proposals and taxonomy, Evolutionary Computation17(3) (2009), 275–306.
18.
Gillala RekhaA.K.T. and Krishna ReddyV., Chaotic salp swarm optimization using svm for class imbalance problems, in: 19th International Conference on Hybrid Intelligent Systems (HIS 2019), Springer, 2019.
19.
Gillala RekhaA.K.T. and Krishna ReddyV., A novel approach for solving skewed classification problem using cluster based ensemble method, Mathematical Foundations of Computing, 2020.
20.
GongJ. and KimH., Rhsboost: improving classification performance in imbalance data, Computational Statistics & Data Analysis111 (2017), 1–13.
21.
HaixiangG.YijingL.ShangJ.MingyunG.YuanyueH. and BingG., Learning from class-imbalanced data: review of methods and applications, Expert Systems with Applications73 (2017), 220–239.
22.
HanH.WangW.-Y. and MaoB.-H., Borderline-smote: a new over-sampling method in imbalanced data sets learning, in: International Conference on Intelligent Computing, Springer, 2005, pp. 878–887.
23.
HeH.BaiY.GarciaE.A. and LiS., Adasyn: Adaptive synthetic sampling approach for imbalanced learning, in: 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), IEEE, 2008, pp. 1322–1328.
24.
JapkowiczN. and StephenS., The class imbalance problem: a systematic study, Intelligent Data Analysis6(5) (2002), 429–449.
25.
JohnsonJ.M. and KhoshgoftaarT.M., Survey on deep learning with class imbalance, Journal of Big Data6(1) (2019), 27.
26.
KubatM.HolteR.C. and MatwinS., Machine learning for the detection of oil spills in satellite radar images, Machine Learning30(2–3) (1998), 195–215.
27.
LaurikkalaJ., Improving identification of difficult small classes by balancing class distribution, in: Conference on Artificial Intelligence in Medicine in Europe, Springer, 2001, pp. 63–66.
28.
LinW.-C.TsaiC.-F.HuY.-H. and JhangJ.-S., Clustering-based undersampling in class-imbalanced data, Information Sciences409 (2017), 17–26.
29.
LópezV.Del RíoS.BenítezJ.M. and HerreraF., Cost-sensitive linguistic fuzzy rule based classification systems under the mapreduce framework for imbalanced big data, Fuzzy Sets and Systems258 (2015), 5–38.
30.
MazurowskiM.A.HabasP.A.ZuradaJ.M.LoJ.Y.BakerJ.A. and TourassiG.D., Training neural network classifiers for medical decision making: the effects of imbalanced datasets on classification performance, Neural Networks21(2–3) (2008), 427–436.
31.
NapieralaK. and StefanowskiJ., Types of minority class examples and their influence on learning classifiers from imbalanced data, Journal of Intelligent Information Systems46(3) (2016), 563–597.
32.
NekooeimehrI. and Lai-YuenS.K., Adaptive semi-unsupervised weighted oversampling (a-suwo) for imbalanced datasets, Expert Systems with Applications46 (2016), 405–416.
PatelH. and ThakurG.S., Classification of imbalanced data using a modified fuzzy-neighbor weighted approach, International Journal of Intelligent Engineering and Systems10(1) (2017), 56–64.
35.
PratiR.C.BatistaG.E. and MonardM.C., Class imbalances versus class overlapping: an analysis of a learning system behavior, in: Mexican International Conference on Artificial Intelligence, Springer, 2004, pp. 312–321.
36.
RekhaG. and TyagiA.K., Necessary information to know to solve class imbalance problem: From a user’s perspective, in: Proceedings of ICRIC 2019, Springer, 2020, pp. 645–658.
37.
RekhaG.TyagiA.K. and Krishna ReddyV., Solving class imbalance problem using bagging, boosting techniques, with and without using noise filtering method, International Journal of Hybrid Intelligent Systems (Preprint) (2019), 1–10.
38.
RekhaG.TyagiA.K. and Krishna ReddyV., A wide scale classification of class imbalance problem and its solutions: a systematic literature review, Journal of Computer Science15 (2019), 886–929.
39.
SáezJ.A.LuengoJ.StefanowskiJ. and HerreraF., Smote-ipf: addressing the noisy and borderline examples problem in imbalanced classification by a re-sampling method with filtering, Information Sciences291 (2015), 184–203.
40.
SunZ.SongQ.ZhuX.SunH.XuB. and ZhouY., A novel ensemble method for classifying imbalanced data, Pattern Recognition48(5) (2015), 1623–1637.
41.
TsaiC.-F.LinW.-C.HuY.-H. and YaoG.-T., Under-sampling class imbalanced datasets by combining clustering analysis and instance selection, Information Sciences477 (2019), 47–54.
42.
VuttipittayamongkolP.ElyanE.PetrovskiA. and JayneC., Overlap-based undersampling for improving imbalanced data classification, in: International Conference on Intelligent Data Engineering and Automated Learning, Springer, 2018, pp. 689–697.
