Imbalanced Classification for Business Analytics

Imbalanced Classification for Business Analytics

Talayeh Razzaghi (University of Central Florida, USA), Andrea Otero (University of Central Florida, USA) and Petros Xanthopoulos (University of Central Florida, USA)
DOI: 10.4018/978-1-5225-1759-7.ch028
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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.

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