TopBackground
Advances in science and technology accelerate the accessibility of raw data and create new opportunities for knowledge discovery. Imbalanced problems can be found in a wide variety of applications, including security surveillance (Wu, Wu, Jiao, Wang, & Chang, 2003), medical diagnosis (Mena & JESUS, 2009; You, Zhao, Li, & Hu, 2011), bioinformatics (Al-Shahib, Breitling, & Gilbert, 2005), geomatics (Kubat, Holte, & Matwin, 1998), telecommunications (Tang, Krasser, Judge, & Zhang, 2006), risk management (Ezawa, Singh, & Norton, 1996), manufacturing (Adam et al., 2011), quality estimation (Lee, Song, Song, & Yoon, 2005), and power management (Hu, Zhu, & Ren, 2008). Imbalanced classification has been studied in a number of studies (N. V. Chawla, 2010; Guo, Yin, Dong, Yang, & Zhou, 2008; He & Garcia, 2009; Su, Mao, Zeng, Li, & Wang, 2009; Sun et al., 2009). Previous works on the classification of imbalanced data (N. V. Chawla, 2010; Kubat et al., 1998; Ngai, Hu, Wong, Chen, & Sun, 2011; Su et al., 2009; Sun et al., 2009) address that many standard classification algorithms achieve poor performance. Therefore, despite the existing amounts of literature there is room for improvement and future contribution.