Author:
Eftekhari Mahdi,Mehrpooya Adel,Saberi-Movahed Farid,Torra Vicenç
Publisher
Springer International Publishing
Reference22 articles.
1. Mehrdad Hosseinzadeh and Mahdi Eftekhari. 2015. Improving rotation forest performance for imbalanced data classification through fuzzy clustering. In The International Symposium on Artificial Intelligence and Signal Processing (AISP), pp. 35–40. IEEE, New York.
2. Rajan, Amee, Chetna Chand, and G. Kulkarni. 2015. Survey on classification algorithms for imbalanced dataset. Int. Inst. Technol. Res. Dev., 1(2).
3. Mahdizadeh, Mahdi, and M. Eftekhari. 2014. Generating fuzzy rule base classifier for highly imbalanced datasets using a hybrid of evolutionary algorithms and subtractive clustering. Journal of Intelligent & Fuzzy Systems 27 (6): 3033–3046.
4. Eftekhari, Mahdi, and Mahboubeh Mahdizadeh. 2015. Proposing a novel cost sensitive imbalanced classification method based on hybrid of new fuzzy cost assigning approaches, fuzzy clustering and evolutionary algorithms. International Journal of Engineering 28 (8): 1160–1168.
5. Ishibuchi, Hisao, Takashi Yamamoto, and Tomoharu Nakashima. 2005. Hybridization of fuzzy GBML approaches for pattern classification problems. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 35 (2): 359–365.