Head of Research Group SCI2S
Soft Computing and Intelligent Information Systems
Universidad de Granada, Spain
"A tour on Imbalanced big data classification and applications"
Big Data applications are emerging during the last years, and researchers from many disciplines are aware of the high advantages related to the knowledge extraction from this type of problem.
The topic of imbalanced classification has gathered a wide attention of researchers during the last several years. It occurs when the classes represented in a problem show a skewed distribution, i.e., there is a minority (or positive) class, and a majority (or negative) one. This case study may be due to rarity of occurrence of a given concept, or even because of some restrictions during the gathering of data for a particular class. In this sense, class imbalance is ubiquitous and prevalent in several applications. The emergence of Big Data brings new problems and challenges for the class imbalance problem.
In this lecture we focus on learning from imbalanced data problems in the context of Big Data, especially when faced with the challenge of Volume. We will analyze the strengths and weaknesses of various MapReduce-based algorithms that address imbalanced data. We will present the current approaches presenting real cases of study and applications, and some research challenges.
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