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A KNN Undersampling Approach for Data Balancing 被引量:3

A KNN Undersampling Approach for Data Balancing
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摘要 In supervised learning, the imbalanced number of instances among the classes in a dataset can make the algorithms to classify one instance from the minority class as one from the majority class. With the aim to solve this problem, the KNN algorithm provides a basis to other balancing methods. These balancing methods are revisited in this work, and a new and simple approach of KNN undersampling is proposed. The experiments demonstrated that the KNN undersampling method outperformed other sampling methods. The proposed method also outperformed the results of other studies, and indicates that the simplicity of KNN can be used as a base for efficient algorithms in machine learning and knowledge discovery. In supervised learning, the imbalanced number of instances among the classes in a dataset can make the algorithms to classify one instance from the minority class as one from the majority class. With the aim to solve this problem, the KNN algorithm provides a basis to other balancing methods. These balancing methods are revisited in this work, and a new and simple approach of KNN undersampling is proposed. The experiments demonstrated that the KNN undersampling method outperformed other sampling methods. The proposed method also outperformed the results of other studies, and indicates that the simplicity of KNN can be used as a base for efficient algorithms in machine learning and knowledge discovery.
出处 《Journal of Intelligent Learning Systems and Applications》 2015年第4期104-116,共13页 智能学习系统与应用(英文)
关键词 MACHINE LEARNING CLASS Overlaping Imbalanced Datases Machine Learning Class Overlaping Imbalanced Datases
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  • 1周传华,徐文倩,朱俊杰.基于代价敏感卷积神经网络的集成分类算法[J].应用科学学报,2022,40(1):69-79. 被引量:6
  • 2ABDI L, HASHEMI S. To combat multi-class imbalanced problems by means of over-sampling and boosting techniques [J]. Soft Computing, 2015, 19(12): 3369-3385.
  • 3VERBIEST N, RAMENTOL E, CORNELIS C, et al. Preprocessing noisy imbalanced datasets using SMOTE enhanced with fuzzy rough prototype selection [J]. Applied Soft Computing, 2014, 22(5): 511-517.
  • 4WANG K J, ADRIAN A M, CHEN K H, et al. A hybrid classifier combining borderline-SMOTE with AIRS algorithm for estimating brain metastasis from lung cancer: a case study in Taiwan [J]. Computer Methods and Programs in Biomedicine, 2015, 119(2): 63-76.
  • 5YU H, NI J, ZHAO J. ACOSampling: an ant colony optimization-based undersampling method for classifying imbalanced DNA microarray data [J]. Neurocomputing, 2013, 101(3): 309-318.
  • 6GARCíA-BORROTO M, MARTíNEZ-TRINIDAD J F, CARRASCO-OCHOA J A. A survey of emerging patterns for supervised classification [J]. Artificial Intelligence Review, 2014, 42(4): 705-721.
  • 7GALAR M, FERNáNDEZ A, BARRENECHEA E, et al. A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches [J]. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 2012, 42(4): 463-484.
  • 8GARCíA V, SáNCHEZ J S, MOLLINEDA R A. On the effectiveness of preprocessing methods when dealing with different levels of class imbalance [J]. Knowledge-Based Systems, 2012, 25(1): 13-21.
  • 9ALEJO R, VALDOVINOS R M, GARCíA V, et al. A hybrid method to face class overlap and class imbalance on neural networks and multi-class scenarios [J]. Pattern Recognition Letters, 2013, 34(4): 380-388.
  • 10KHAZAI S, SAFARI A, MOJARADI B, et al. Improving the SVDD approach to hyperspectral image classification [J]. IEEE Geoscience and Remote Sensing Letters, 2012, 9(4): 594-598.

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