Imbalanced Classification for Big Data

New developments in computation have allowed an explosion for both data generation and storage. The high value that is hidden within this large volume of data has attracted more and more researchers to address the topic of Big Data analytics. The main difference between addressing Big Data applications and carrying out traditional DM tasks is scalability. To overcome this issue, the MapReduce framework has arisen as a “de facto” solution. Basically, it carries out a “divide-and-conquer” distributed procedure in a fault-tolerant way (supported by a distributed file system) to adapt for commodity hardware. Apart from the difficulties in addressing the Big Data problem itself, we must take into account that the events of interest might occur infrequently. Having in mind the challenges of mining rare classes in standard classification tasks, adding this to the problem of addressing high volumes of data impose a strong constraint for the development of both accurate and scalable solutions. In order to present this interesting topic, current chapter is organized as follows. First, Sect. 13.1 provides a quick overview on Big Data analytics in the context of imbalanced classification. Then, Sect. 13.2 presents the topic of Big Data in detail, focusing on the MapReduce programming model, the Spark framework, and those software libraries that includes Big Data implementations for ML algorithms. Section 13.3 shows an overview on those works that address imbalanced classification for Big Data problems. Then, Sect. 13.4 presents a discussion on the challenges and open problems on imbalanced Big Data classification. Finally, Sect. 13.5 summarizes and concludes this chapter.

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