Online cost-sensitive neural network classifiers for non-stationary and imbalanced data streams

Neural Computing and Applications - Tập 23 Số 5 - Trang 1283-1295 - 2013
Adel Ghazikhani1, Reza Monsefi1, Hadi Sadoghi Yazdi1
1Computer Engineering Department, Ferdowsi University of Mashhad, Mashhad, Iran

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Tài liệu tham khảo

Aggarwal CC (2006) Data streams: models and algorithms. Springer, Berlin

Klinkenberg R, Joachims T (2000) Detecting concept drift with support vector machines. Paper presented at the 17th international conference on machine learning, San Mateo

Sun J, Li H (2011) Dynamic financial distress prediction using instance selection for the disposal of concept drift. Expert Syst Appl 38(3):2566–2576

Martínez-Rego D, Pérez-Sánchez B, Fontenla-Romero O, Alonso-Betanzos A (2011) A robust incremental learning method for non-stationary environments. Neurocomputing 74(11):1800–1808

Pavlidis NG, Tasoulis DK, Adams NM, Hand DJ (2011) An adaptive classifier for data streams. Pattern Recognit 44(1):78–96

Elwell R, Polikar R (2011) Incremental learning of concept drift in nonstationary environments. IEEE Trans Neural Netw 22(1):1517–1531

Abdulsalam H, Skillicorn DB, Martin P (2011) Classification using streaming random forests. IEEE Trans Knowl Data Eng 23(1):22–36

Masud MM, Jing G, Khan L, Jiawei H, Thuraisingham BM (2011) Classification and novel class detection in concept-drifting data streams under time constraints. IEEE Trans Knowl Data Eng 23(6):859–874

Gao J, Fan W, Han J, Yu PS (2007) A general framework for mining concept-drifting data streams with skewed distributions. Paper presented at the SIAM

Lichtenwalter R, Chawla NV (2009) Learning to classify data streams with imbalanced class distributions. Paper presented at the PAKDD

Lichtenwalter R, Chawla NV (2009) Adaptive methods for classification in arbitrarily imbalanced and drifting data streams. Paper presented at the PAKDD workshop for data mining when classes are imbalanced and errors have costs

Chen S, He H (2010) Towards incremental learning of nonstationary imbalanced data stream: a multiple selectively recursive approach. Evol Syst 2(1):35–50

Ditzler G, Polikar R (2010) An ensemble based incremental learning framework for concept drift and class imbalance. Paper presented at the WCCI

Tsymbal A (2004) The problem of concept drift: definitions and related work. Technical Report: TCD-CS-2004-15. Trinity College Dublin, Computer Science Department, Dublin

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

Zadrozny B, Langford J, Abe N (2003, Nov) Cost-sensitive learning by cost proportionate example weighting. Paper presented at the 3rd IEEE international conference on data mining, Melbourne

Ling CX, Li C (2004, July) Decision trees with minimal costs. Paper presented at the 21st International Conference on Machine Learning, Banff

Domingos P (1999) Metacost: a general method for making classifiers cost-sensitive. In: Paper presented at the proceedings of the fifth ACM SIGKDD international conference on knowledge discovery and data mining. San Diego, CA

Lan J-s, Berardi V, Patuwo B, Hu M (2009) A joint investigation of misclassification treatments and imbalanced datasets on neural network performance. Neural Comput Appl 18(7):689–706. doi: 10.1007/s00521-009-0239-1

Yoon K, Kwek S (2007) A data reduction approach for resolving the imbalanced data issue in functional genomics. Neural Comput Appl 16(3):295–306. doi: 10.1007/s00521-007-0089-7

Zhi-Hua Z, Xu-Ying L (2006) Training cost-sensitive neural networks with methods addressing the class imbalance problem. IEEE Trans Knowl Data Eng 18(1):63–77

Street NW, Kim Y (2001) A streaming ensemble algorithm (SEA) for large-scale classification. Paper presented at the 7th ACM SIGKDD international conference on knowledge discovery and data mining

Widmer G, Kubat M (1996) Learning in the presence of concept drift and hidden contexts. Mach Learn 23:60–101

Narasimhamurthy A, Kuncheva LI (2007) A framework for generating data to simulate changing environments. Paper presented at the IASTED international conference on artificial intelligence and applications

Harries M (1999) Splice-2 comparative evaluation: electricity pricing. University of South Wales

Neurotech (2009) PAKDD 2009 data mining competition. http://sede.neurotech.com.br:443/PAKDD2009/

NOAA (2010) Weather data. http://users.rowan.edu/~polikar/research/NSE/

UCI Repository of Machine Learning Database (2007) School of information and computer science. University of California, Irvine. http://www.ics.uci.edu/~mlearn/MLRepository.html

Yang Y, Wu X, Zhu X (2006) Mining in anticipation for concept change: proactive-reactive prediction in data streams. Data Mining Knowl Discov 13(3):261–289

Alpaydın E (2010) Introduction to machine learning, 2nd edn. The MIT Press, Cambridge