A Classification Method for Imbalanced Data Based on SMOTE and Fuzzy Rough Nearest Neighbor Algorithm
FRNN (Fuzzy Rough Nearest Neighbor) algorithm has exhibited good performance in classifying data with inadequate features. However, FRNN does not perform well on imbalanced data. To overcome this problem, this paper introduces a combination method. An improved SMOTE method is adopted to balance data and FRNN is applied as the classification method. Experiments show that the combination method can obtain a better result rather than classical FRNN algorithm.
KeywordsImbalanced data SMOTE Fuzzy rough set Nearest neighbor Classification
We would like to acknowledge the support for this work from the National Natural Science Foundation of China (Grant Nos. 61403200, 61170180), Natural Science Foundation of Jiangsu Province (Grant No.BK20140800).
- 2.Kotsiantis, S.B., Zaharakis, I.: Supervised machine learning: a review of classification techniques. Emerg. Artif. Intell. Appl. Comput. Eng., 3–24 (2007)Google Scholar
- 3.Maloof, M.A.: Learning when data sets are imbalanced and when costs are unequal and unknown. In: ICML-2003 Workshop on Learning from Imbalanced Data Sets II, vol. 2, pp. 1–2 (2003)Google Scholar
- 4.Japkowicz, N.: Learning from imbalanced data sets: a comparison of various strategies. In: AAAI Workshop on Learning from Imbalanced Data Sets, pp. 10–15 (2000)Google Scholar
- 8.Ramentol, E., Vluymans, S., Verbiest, N., Caballero, Y.: IFROWANN: imbalanced fuzzy-rough ordered weighted average nearest neighbor classification, pp. 1–15 (2014)Google Scholar
- 14.Liu, X., Liu, S.: New oversampling algorithm DB-SMOTE. Comput. Eng. Appl., 92–95 (2014)Google Scholar
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (http://creativecommons.org/licenses/by-nc/2.5/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.