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加权极限学习机研究现状和参考文献(3)

时间:2022-05-21 19:45来源:毕业论文
[37] Seiffert C, Khoshgoftaar T M, Van Hulse J, et al。 RUSBoost: A hybrid approach to alleviating class imbalance[J]。 Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on
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[37] Seiffert C, Khoshgoftaar T M, Van Hulse J, et al。 RUSBoost: A hybrid approach to alleviating class imbalance[J]。 Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on, 2010, 40(1): 185-197

[38] Liu X Y, Wu J, Zhou Z H。 Exploratory undersampling  for  class-imbalance learning[J]。 Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 2009, 39(2): 539-550

[39] Sun Z, Song Q, Zhu X, et al。 A novel ensemble method for classifying imbalanced data[J]。 Pattern Recognition, 2015, 48(5): 1623-1637

[40] Diez-Pastor J F, Rodriguez J J, Garcia-Osorio C I, et al。 Diversity techniques improve the performance of the best imbalance learning ensembles[J]。 Information Sciences, 2015, 325: 98-117

[41] Fletcher R。 Practical Methods in Optimization, vol。 2Wiley[J]。 Chichester, USA, 1981 [42] Lin C F, Wang S D。 Fuzzy support vector machines[J]。 Neural Networks, IEEE Transactions on, 2002, 13(2): 464-471

[43] Yu H, Sun C, Yang W, et al。 AL-ELM: One uncertainty-based active learning algorithm using extreme learning machine [J]。 Neurocomputing, 2015, 166: 140-150。

[44] Alcalá-Fdez J, Fernandez A, Luengo J, et al。 KEEL Data-Mining Software Tool: Data Set  Repository,  Integration  of  Algorithms  and  Experimental  Analysis  Framework  [J]。

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[45] Yu H, Mu C, Sun C, et al。 Support Vector Machine-Based Optimized Decision Threshold Adjustment Strategy for Classifying Imbalanced Data [J]。 Knowledge-Based Systems, 2015, 76: 67-78


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