Efficient Imbalanced Multimedia Concept Retrieval by Deep Learning on Spark Clusters

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Author(s): Yilin Yan (University of Miami, USA), Min Chen (University of Washington Bothell, USA), Saad Sadiq (University of Miami, USA)and Mei-Ling Shyu (University of Miami, USA)
Copyright: 2020
Pages: 21
Source title: Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-7998-0414-7.ch017

Keywords: Artificial Intelligence / Computer Science & IT / Engineering Science Reference / Neural Networks

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Abstract

The classification of imbalanced datasets has recently attracted significant attention due to its implications in several real-world use cases. The classifiers developed on datasets with skewed distributions tend to favor the majority classes and are biased against the minority class. Despite extensive research interests, imbalanced data classification remains a challenge in data mining research, especially for multimedia data. Our attempt to overcome this hurdle is to develop a convolutional neural network (CNN) based deep learning solution integrated with a bootstrapping technique. Considering that convolutional neural networks are very computationally expensive coupled with big training datasets, we propose to extract features from pre-trained convolutional neural network models and feed those features to another full connected neutral network. Spark implementation shows promising performance of our model in handling big datasets with respect to feasibility and scalability.

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Efficient Imbalanced Multimedia Concept Retrieval by Deep Learning on Spark Clusters

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