@incollection{,
	
					
					
					772B7093DC85D89D8917ADB1274EBFBB
				,
	author={{B.Ramakrishna} and {JNTU}},
	journal={{Global Journal of Computer Science and Technology}},
	journal={{GJCST}}0975-41720975-435010.34257/gjcst,
	address={Cambridge, United States},
	publisher={Global Journals Organisation}17218
}

@incollection{b0,
	
	
		,
	title={{The digital universe in 2020: Big data, bigger digital shadows, and biggest growth in the far east}}
		,
	author={{
			JohnGantz
		} and {
			DavidReinsel
		}}
		
	
	
		,
	booktitle={{IDC iView: IDC Analyze the future}}
				
			,
	year={2007. 2012. 2012}
			
		
	

}

@incollection{b1,
	
	
		,
	title={{Incremental learning from noisy data}}
		,
	author={{
			JeffreyCSchlimmer
		} and {
			RichardHGranger
		}}
		
	
	
		,
	journal={{Machine learning}}
		
			1
			
			,
	year={1986}
		
	

}

@incollection{b2,
	
	
		,
	title={{Learning in the presence of concept drift and hidden contexts}}
		,
	author={{
			GerhardWidmer
		} and {
			Miroslavkubat
		}}
		
	
	
		,
	journal={{Machine learning}}
		
			23
			
			,
	year={1996}
		
	

}

@incollection{b3,
	
	
		,
	title={{Machine learning for the detection of oil spills in satellite radar images}}
		,
	author={{
			Kubat
		} and {
			RobertCMiroslav
		} and {
			StanHolte
		} and {
			Matwin
		}}
		
		
		
	
	
		,
	journal={{Machine learning}}
		
			30
			
			,
	year={1998}
		
	

}

@incollection{b4,
	
	
		,
	title={{The class imbalance problem: A systematic study}}
		,
	author={{
			NathalieJapkowicz
		} and {
			ShajuStephen
		}}
		
	
	
		,
	journal={{Intelligent data analysis}}
		
			6
			
			,
	year={2002}
		
	

}

@incollection{b5,
	
	
		,
	title={{Addressing the curse of imbalanced training sets: one-sided selection}}
		,
	author={{
			MiroslavKubat
		} and {
			StanMatwin
		}}
		
	
	
		,
	journal={{ICML}}
		
			97
			,
	year={1997}
		
	

}

@incollection{b6,
	
	
		,
	title={{A study of the behavior of several methods for balancing machine learning training data}}
		,
	author={{
			GustavoEapaBatista
		} and {
			CRonaldo
		} and {
			MariaCarolinaPrati
		} and {
			Monard
		}}
		
		
		
	
	
		,
	journal={{ACM Sigkdd Explorations Newsletter}}
		
			6
			
			,
	year={2004}
		
	

}

@incollection{b7,
	
	
		,
	title={{kNN approach to unbalanced data distributions: a case study involving information extraction}}
		,
	author={{
			InderjeetMani
		} and {
			IZhang
		}}
		
	
	
		,
	booktitle={{Proceedings of workshop on learning from imbalanced datasets}}
				workshop on learning from imbalanced datasets
		
			,
	year={2003}
		
	

}

@incollection{b8,
	
	
		,
	title={{Issues in mining imbalanced data sets-a review paper}}
		,
	author={{
			SofiaVisa
		} and {
			Ancaralescu
		}}
		
	
	
		,
	booktitle={{Proceedings of the sixteen midwest artificial intelligence and cognitive science conference}}
		,
	booktitle={{sn}}
		the sixteen midwest artificial intelligence and cognitive science conference
		
			,
	year={2005. 2005}
		
	

}

@incollection{b9,
	
	
		,
	title={{On predicting rare classes with SVM ensembles in scene classification}}
		,
	author={{
			RongYan
		}}
	
	
		,
	booktitle={{Proceedings.(ICASSP'03). 2003 IEEE International Conference on}}
				.(ICASSP'03). 2003 IEEE International Conference on
		
			,
	publisher={IEEE}
			,
	year={2003. 2003}
			3
		
	

}

@incollection{b10,
	
	
		,
	title={{Class-boundary alignment for imbalanced dataset learning}}
		,
	author={{
			GangWu
		} and {
			EdwardYChang
		}}
		
	
	
		,
	booktitle={{ICML 2003 workshop on learning from imbalanced data sets II}}
				Washington, DC
		
			,
	year={2003}
		
	

}

@incollection{b11,
	
	
		,
	title={{Experimental perspectives on learning from imbalanced data}}
		,
	author={{
			Van Hulse
		} and {
			TaghiMJason
		} and {
			AmriKhoshgoftaar
		} and {
			Napolitano
		}}
		
		
		
	
	
		,
	booktitle={{Proceedings of the 24th international conference on Machine learning}}
				the 24th international conference on Machine learning
		
			,
	publisher={ACM}
			,
	year={2007}
		
	

}

@incollection{b12,
	
	
		,
	title={{SMOTE: synthetic minority over-sampling technique}}
		,
	author={{
			NiteshVChawla
		}}
	
	
		,
	journal={{Journal of artificial intelligence research}}
		
			16
			
			,
	year={2002}
		
	

}

@incollection{b13,
	
	
		,
	title={{An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics}}
		,
	author={{
			VictoriaLópez
		}}
	
	
		,
	journal={{Information Sciences}}
		
			250
			
			,
	year={2013}
		
	

}

@book{b14,
	
	
		,
	title={{Living in an imbalanced world}}
		,
	author={{
			ThomasHoens
		} and {
			Ryan
		}}
		
		
			,
	year={2012}
		
		
			University of Notre Dame
		
	

}

@incollection{b15,
	
	
		,
	title={{DDD: A new ensemble approach for dealing with concept drift}}
		,
	author={{
			LeandroLMinku
		} and {
			XinYao
		}}
		
	
	
		,
	journal={{IEEE transactions on knowledge and data engineering}}
		
			24
			
			,
	year={2012}
		
	

}

@incollection{b16,
	
	
		,
	title={{The impact of diversity on online ensemble learning in the presence of concept drift}}
		,
	author={{
			LeandroLMinku
		} and {
			PAllan
		} and {
			XinWhite
		} and {
			Yao
		}}
		
		
		
	
	
		,
	journal={{IEEE Transactions on Knowledge and Data Engineering}}
		
			22
			
			,
	year={2010}
		
	

}

@incollection{b17,
	
	
		,
	title={{The impact of changing populations on classifier performance}}
		,
	author={{
			MarkGKelly
		} and {
			JDavid
		} and {
			NiallMHand
		} and {
			Adams
		}}
		
		
		
	
	
		,
	booktitle={{Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining}}
				the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
		
			,
	publisher={ACM}
			,
	year={1999}
		
	

}

@incollection{b18,
	
	
		,
	title={{Incremental learning of concept drift in nonstationary environments}}
		,
	author={{
			RyanElwell
		} and {
			Robipolikar
		}}
		
	
	
		,
	journal={{IEEE Transactions on Neural Networks}}
		
			22
			
			,
	year={2011}
		
	

}

@incollection{b19,
	
	
		,
	title={{Learning from streaming data with concept drift and imbalance: an overview}}
		,
	author={{
			THoens
		} and {
			RobipolikarRyan
		} and {
			NiteshVChawla
		}}
		
		
	
	
		,
	journal={{Progress in Artificial Intelligence}}
		
			1
			1
			
			,
	year={2012}
		
	

}

@incollection{b20,
	
	
		,
	title={{Graph ensemble boosting for imbalanced noisy graph stream classification}}
		,
	author={{
			Pan
		} and {
			Shirui
		}}
		
	
	
		,
	journal={{IEEE transactions on cybernetics}}
		
			45
			
			,
	year={2015}
		
	

}

@incollection{b21,
	
	
		,
	title={{A new dynamic modeling framework for credit risk assessment}}
		,
	author={{
			MariaSousa
		} and {
			JoãoRocha
		} and {
			ElísiobrandãoGama
		}}
		
		
	
	
		,
	journal={{Expert Systems with Applications}}
		
			45
			
			,
	year={2016}
		
	

}

@incollection{b22,
	
	
		,
	title={{Classifying data streams with skewed class distributions and concept drifts}}
		,
	author={{
			JingGao
		}}
	
	
		,
	journal={{IEEE Internet Computing}}
		
			12
			6
			,
	year={2008}
		
	

}

@incollection{b23,
	
	
		,
	title={{A general framework for mining concept-drifting data streams with skewed distributions}}
		,
	author={{
			JingGao
		}}
	
	
		,
	booktitle={{Proceedings of the 2007 SIAM International Conference on Data Mining}}
		,
	booktitle={{Society for Industrial and Applied Mathematics}}
		the 2007 SIAM International Conference on Data Mining
		
			,
	year={2007}
		
	

}

@incollection{b24,
	
	
		,
	title={{Classifying imbalanced data streams via dynamic feature group weighting with importance sampling}}
		,
	author={{
			KeWu
		}}
	
	
		,
	booktitle={{Proceedings of the 2014 SIAM International Conference on Data Mining}}
				the 2014 SIAM International Conference on Data Mining
		
			,
	year={2014}
		
	

}

@incollection{b25,
	
	
		,
	title={{Towards incremental learning of non stationary imbalanced data stream: a multiple selectively recursive approach}}
		,
	author={{
			ShengChen
		} and {
			HaiboHe
		}}
		
	
	
		,
	journal={{Evolving Systems}}
		
			2
			1
			
			,
	year={2011}
		
	

}

@incollection{b26,
	
	
		,
	title={{Sera: selectively recursive approach towards non stationary imbalanced stream data mining}}
		,
	author={{
			ShengChen
		} and {
			HaiboHe
		}}
		
	
	
		,
	booktitle={{IJCNN 2009. International Joint Conference on}}
				
			,
	publisher={IEEE}
			,
	year={2009. 2009}
		
	

}

@incollection{b27,
	
	
		,
	title={{Incremental learning of concept drift from streaming imbalanced data}}
		,
	author={{
			GregoryDitzler
		} and {
			Robipolikar
		}}
		
	
	
		,
	journal={{ieee transactions on knowledge and data engineering}}
		
			25
			
			,
	year={2013}
		
	

}

@incollection{b28,
	
	
		,
	title={{Learning in non-stationary environments with class imbalance}}
		,
	author={{
			ThomasHoens
		} and {
			NiteshVRyan
		} and {
			Chawla
		}}
		
		
	
	
		,
	booktitle={{Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining}}
				the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
		
			,
	publisher={ACM}
			,
	year={2012}
		
	

}

@incollection{b29,
	
	
		,
	title={{Heuristic updatable weighted random subspaces for non-stationary environments}}
		,
	author={{
			THoens
		} and {
			NiteshVRyan
		} and {
			RobipolikarChawla
		}}
		
		
	
	
		,
	booktitle={{Data Mining (ICDM), 2011 IEEE 11th International Conference on}}
				
			,
	publisher={IEEE}
			,
	year={2011}
		
	

}

@incollection{b30,
	
	
		,
	title={{Learning in nonstationary environments: A survey}}
		,
	author={{
			GregoryDitzler
		}}
	
	
		,
	journal={{IEEE Computational Intelligence Magazine}}
		
			10
			
			,
	year={2015}
		
	

}

@incollection{b31,
	
	
		,
	title={{Concept drift detection for online class imbalance learning}}
		,
	author={{
			ShuoWang
		}}
	
	
		,
	booktitle={{The 2013 International Joint Conference on}}
				
			,
	publisher={IEEE}
			,
	year={2013}
		
	
	,
	note={Neural Networks (IJCNN)}

}

@incollection{b32,
	
	
		,
	title={{Concept drift detection for streaming data}}
		,
	author={{
			HengWang
		} and {
			ZubinAbraham
		}}
		
	
	
		,
	booktitle={{2015 International Joint Conference on}}
				
			,
	publisher={IEEE}
			,
	year={2015}
		
	

}

@book{b33,
	
	
		,
	title={{Prequential AUC for classifier evaluation and drift detection in evolving data streams}}
		,
	author={{
			DariuszBrzezinski
		} and {
			JerzyStefanoWski
		}}
		
		
			,
	year={2014}
			,
	publisher={Springer International Publishing}
		
	
	,
	note={International Workshop on New Frontiers in Mining Complex Patterns}

}

@book{b34,
	
	
		,
	title={{Prequential AUC: Properties of the Area Under the ROC Curve for Data Streams with Concept Drift}}
		,
	author={{
			DariuszBrzezinski
		} and {
			JerzyStefanoWski
		}}
		
		
	

}

@incollection{b35,
	
	
		,
	title={{Ensemble of subset online sequential extreme learning machine for class imbalance and concept drift}}
		,
	author={{
			BilalMirza
		} and {
			ZhipingLin
		} and {
			NanLiu
		}}
		
		
	
	
		,
	journal={{Neuro computing}}
		
			149
			
			,
	year={2015}
		
	

}

@incollection{b36,
	
	
		,
	title={{Recursive least square perceptron model for non-stationary and imbalanced data stream classification}}
		,
	author={{
			AdelGhazikhani
		} and {
			RezaMonsefi
		} and {
			Hadisadoghiyazdi
		}}
		
		
	
	
		,
	journal={{Evolving Systems}}
		
			4
			
			,
	year={2013}
		
	

}

@incollection{b37,
	
	
		,
	title={{Online neural network model for nonstationary and imbalanced data stream classification}}
		,
	author={{
			AdelGhazi Khani
		} and {
			RezaMonsefi
		} and {
			Hadi SadoghiYazdi
		}}
		
		
	
	
		,
	journal={{International Journal of Machine Learning and Cybernetics}}
		
			5
			
			,
	year={2014}
		
	

}

@incollection{b38,
	
	
		,
	title={{A learning framework for online class imbalance learning}}
		,
	author={{
			Wang
		} and {
			LeandroLShuo
		} and {
			XinMinku
		} and {
			Yao
		}}
		
		
		
	
	
		,
	journal={{Computational Intelligence and Ensemble Learning (CIEL)}}
		
			,
	year={2013. 2013}
			,
	publisher={IEEE}
		
	
	,
	note={IEEE Symposium on}

}

@incollection{b39,
	
	
		,
	title={{A streaming ensemble algorithm (SEA) for large-scale classification}}
		,
	author={{
			WStreet
		} and {
			Yong SeogNick
		} and {
			Kim
		}}
		
		
	
	
		,
	booktitle={{Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining}}
				the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
		
			,
	publisher={ACM}
			,
	year={2001}
		
	

}

@incollection{b40,
	
	
		,
	title={{Dynamic integration of classifiers for handling concept drift}}
		,
	author={{
			AlexeyTsymbal
		}}
	
	
		,
	journal={{Information fusion}}
		
			9
			
			,
	year={2008}
		
	

}

@incollection{b41,
	
	
		,
	title={{Reacting to different types of concept drift: The accuracy updated ensemble algorithm}}
		,
	author={{
			DariuszBrzezinski
		} and {
			JerzyStefanowski
		}}
		
	
	
		,
	journal={{IEEE Transactions on Neural Networks and Learning Systems}}
		
			25
			
			,
	year={2014}
		
	

}

@incollection{b42,
	
	
		,
	title={{Learn++: An incremental learning algorithm for supervised neural networks}}
		,
	author={{
			RobiPolikar
		}}
	
	
		,
	booktitle={{IEEE transactions on systems, man, and cybernetics, part C (applications and reviews)}}
				
			,
	year={2001}
			31
			
		
	

}

@incollection{b43,
	
	
		,
	title={{Tackling concept drift by temporal inductive transfer}}
		,
	author={{
			GeorgeForman
		}}
	
	
		,
	booktitle={{Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval}}
				the 29th annual international ACM SIGIR conference on Research and development in information retrieval
		
			,
	publisher={ACM}
			,
	year={2006}
		
	

}

@incollection{b44,
	
	
		,
	title={{Diversity creation methods: a survey and categorisation}}
		,
	author={{
			GavinBrown
		}}
	
	
		,
	journal={{Information Fusion}}
		
			6
			
			,
	year={2005}
		
	

}

@incollection{b45,
	
	
		,
	title={{An analysis of diversity measures}}
		,
	author={{
			ETang
		} and {
			PonnuthuraiNKe
		} and {
			Suganthan
		} and {
			Yao
		}}
		
		
		
	
	
		,
	journal={{Machine learning}}
		
			65
			1
			
			,
	year={2006}
		
	

}

@incollection{b46,
	
	
		,
	title={{Exponentially weighted moving average charts for detecting concept drift}}
		,
	author={{
			GordonJRoss
		}}
	
	
		,
	journal={{Pattern Recognition Letters}}
		
			33
			
			,
	year={2012}
		
	

}

@book{b47,
	
	
		,
	title={{New drift detection method for data streams}}
		,
	author={{
			ParinazSobhani
		} and {
			HamidBeigy
		}}
		
		
			,
	year={2011}
			,
	publisher={Springer}
			,
	address={Berlin Heidelberg}
		
	

}

@incollection{b48,
	
	
		,
	title={{Paired learners for concept drift}}
		,
	author={{
			StephenHBach
		} and {
			MarcusAMalo
		}}
		
	
	
		,
	booktitle={{Data Mining, 2008. ICDM'08. Eighth IEEE International Conference on}}
				
			,
	publisher={IEEE}
			,
	year={2008}
		
	

}

@book{b49,
	
	
		,
	title={{Concept Drift Detection Through Re sampling}}
		,
	author={{
			MaayanHarel
		}}
		ICML. 2014
		
	

}

@incollection{b50,
	
	
		,
	title={{A study of cross-validation and bootstrap for accuracy estimation and model selection}}
		,
	author={{
			RonKohavi
		}}
	
	
		,
	journal={{Ijcai}}
		
			14
			2
			,
	year={1995}
		
	

}

@incollection{b51,
	
	
		,
	title={{Olindda: A clusterbased approach for detecting novelty and concept drift in data streams}}
		,
	author={{
			EduardoJSpinosa
		} and {
			AndréPonce De Leon F De Carvalho
		} and {
			JoãoGama
		}}
		
		
	
	
		,
	booktitle={{Proceedings of the 2007 ACM symposium on Applied computing}}
				the 2007 ACM symposium on Applied computing
		
			,
	publisher={ACM}
			,
	year={2007}
		
	

}

@incollection{b52,
	
	
		,
	title={{Novelty detection algorithm for data streams multi-class problems}}
		,
	author={{
			ElaineRFaria
		} and {
			JoãoGama
		} and {
			André CplfCarvalho
		}}
		
		
	
	
		,
	booktitle={{Proceedings of the 28th annual ACM symposium on applied computing}}
				the 28th annual ACM symposium on applied computing
		
			,
	publisher={ACM}
			,
	year={2013}
		
	

}

@incollection{b53,
	
	
		,
	title={{An efficient method of building an ensemble of classifiers in streaming data}}
		,
	author={{
			JoungRyu
		} and {
			Woo
		}}
		
	
	
		,
	booktitle={{International Conference on Big Data Analytics}}
				Berlin Heidelberg
		
			,
	publisher={Springer}
			,
	year={2012}
		
	

}

@incollection{b54,
	
	
		,
	title={{Classification and novel class detection in concept-drifting data streams under time constraints}}
		,
	author={{
			MohammadMasud
		}}
	
	
		,
	journal={{IEEE Transactions on Knowledge and Data Engineering}}
		
			23
			
			,
	year={2011}
		
	

}

@incollection{b55,
	
	
		,
	title={{A dct based approach for detecting novelty and concept drift in data streams}}
		,
	author={{
			MortezaziHayat
		} and {
			MahmoudRezaHashemi
		}}
		
	
	
		,
	booktitle={{Soft Computing and Pattern Recognition (SoCPaR), 2010 International Conference of}}
				
			,
	publisher={IEEE}
			,
	year={2010}
		
	

}

@incollection{b56,
	
	
		,
	title={{A grid density based framework for classifying streaming data in the presence of concept drift}}
		,
	author={{
			TegjyotSethi
		} and {
			MehmedkantardzicSingh
		} and {
			HanquingHu
		}}
		
		
	
	
		,
	journal={{Journal of Intelligent Information Systems}}
		
			46
			
			,
	year={2016}
		
	

}

@incollection{b57,
	
	
		,
	title={{Detection of concept drift for learning from stream data}}
		,
	author={{
			JeonghoonLee
		} and {
			FredericMagoules
		}}
		
	
	
		,
	booktitle={{High Performance Computing and Communication & 2012 IEEE 9th International Conference on Embedded Software and Systems (HPCC-ICESS)}}
				
			,
	publisher={IEEE}
			,
	year={2012. 2012}
		
	
	,
	note={IEEE 14th International Conference on}

}

@incollection{b58,
	
	
		,
	title={{Computational Intelligence in Dynamic and Uncertain Environments (CIDUE)}}
		,
	author={{
			GregoryDitzler
		} and {
			Robipolikar
		}}
		
	
	
		,
	journal={{IEEE Symposium on}}
		
			,
	year={2011. 2011}
			,
	publisher={IEEE}
		
	
	,
	note={Hellinger distance based drift detection for nonstationary environments}

}

@incollection{b59,
	
	
		,
	title={{PCA feature extraction for change detection in multidimensional unlabeled data}}
		,
	author={{
			LudmilaIKuncheva
		} and {
			WilliamJFaithfull
		}}
		
	
	
		,
	booktitle={{IEEE transactions on neural networks and learning systems}}
				
			,
	year={2014}
			25
			
		
	

}

@incollection{b60,
	
	
		,
	title={{A pca-based change detection framework for multidimensional data streams: Change detection in multidimensional data streams}}
		,
	author={{
			AbdulhakimAQahtan
		}}
	
	
		,
	booktitle={{Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining}}
				the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
		
			,
	publisher={ACM}
			,
	year={2015}
		
	

}

@incollection{b61,
	
	
		,
	title={{Adaptive concept drift detection}}
		,
	author={{
			AntonDries
		} and {
			UlrichRückert
		}}
		
	
	
		,
	journal={{Statistical Analysis and Data Mining}}
		
			2
			
			,
	year={2009}
		
	

}

@incollection{b62,
	
	
		,
	title={{Drift detection using uncertainty distribution divergence}}
		,
	author={{
			PatrickLindstrom
		} and {
			BrianMacNamee
		} and {
			SarahJaneDelany
		}}
		
		
	
	
		,
	journal={{Evolving Systems}}
		
			4
			
			,
	year={2013}
		
	

}

@incollection{b63,
	
	
		,
	title={{We're not in Kansas anymore: detecting domain changes in streams}}
		,
	author={{
			MarkDredze
		} and {
			TimOates
		} and {
			ChristinePiatko
		}}
		
		
	
	
		,
	booktitle={{Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing}}
				the 2010 Conference on Empirical Methods in Natural Language Processing
		
			,
	publisher={Association for Computational Linguistics}
			,
	year={2010}
		
	

}

@incollection{b64,
	
	
		,
	title={{Change with delayed labeling: When is it detectable?}}
		,
	author={{
			Indre?liobaite
		}}
	
	
		,
	booktitle={{Data Mining Workshops (ICDMW), 2010 IEEE International Conference on}}
				
			,
	publisher={IEEE}
			,
	year={2010}
		
	

}

@incollection{b65,
	
	
		,
	title={{A survey on concept drift adaptation}}
		,
	author={{
			JoãoGama
		}}
	
	
		,
	journal={{ACM Computing Surveys (CSUR)}}
		
			46
			44
			,
	year={2014}
		
	

}

@incollection{b66,
	
	
		,
	title={{Next challenges for adaptive learning systems}}
		,
	author={{
			IndreZliobaite
		}}
	
	
		,
	journal={{ACM SIGKDD Explorations Newsletter}}
		
			14
			
			,
	year={2012}
		
	

}