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Deep Learning Based Imbalanced Data Classification and Information Retrieval for Multimedia Big Data

Deep Learning Based Imbalanced Data Classification and Information Retrieval for Multimedia Big Data

Hot PhD Thesis on Deep Learning Based Imbalanced Data Classification and Information Retrieval for Multimedia Big Data

Research Area:  Machine Learning

Abstract:

The development in information science has enabled an explosive growth of data, which attracts more and more researchers to engage in the field of big data analytics. Noticeably, in many real-world applications, large amounts of data are imbalanced data since the events of interests occur infrequently. Classification of imbalanced data is an important research problem as lots of real-world datasets have skewed class distributions in which the majority of instances (examples) belong to one class and far fewer instances belong to the others. A classifier induced from an imbalanced dataset is more likely to be biased towards the majority classes and shows very poor classification accuracy on the minority classes. While in many applications, the minority instances actually represent the concept of interest (e.g., fraud in banking operations, abnormal cell in medical data, etc.), and the detection of these rare events has become more important. Despite extensive research efforts, rare event mining remains one of the most challenging problems in information retrieval, especially for multimedia big data. To tackle this challenge, in this dissertation, we propose an extended deep learning approach to achieve promising performance in classifying largely skewed multimedia dataset.

Name of the Researcher:  Yilin Yan

Name of the Supervisor(s):  Mei-Ling Shyu

Year of Completion:  2018

University:  University of Miami

Thesis Link:   Home Page Url