Name
Abstract | ||
|---|---|---|
Numerous deep learning architectures have been designed for a variety of tasks in the past few years. However, it is almost impossible for one model to work well for all kinds of scenarios and datasets. Therefore, we present an ensemble deep learning framework in this paper, which not only decreases the information loss and over-fitting problems caused by single models, but also overcomes the imbalanced data issue in multimedia big data. First, a suite of deep learning algorithms are utilized for deep feature selection. Thereafter, an enhanced ensemble algorithm is developed based on the performance of each single Support Vector Machine classifier on each deep feature set. We evaluate our proposed ensemble deep learning framework on a large and highly imbalanced video dataset containing natural disaster events. Experimental results demonstrate the effectiveness of the proposed framework for semantic event detection, and show how it outperforms several state-of-the-art deep learning architectures, as well as handcrafted features integrated with ensemble and non-ensemble algorithms. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/ISM.2016.0048 | 2016 IEEE International Symposium on Multimedia (ISM) |
Keywords | Field | DocType |
Deep learning,Ensemble learning,Imbalanced data,Semantic event detection,Multimedia big data | Data mining,Suite,Feature selection,Computer science,Deep belief network,Feature set,Artificial intelligence,Deep learning,Ensemble learning,Multimedia big data,Information loss,Pattern recognition,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-5090-4572-3 | 2 | 0.36 |
References | Authors | |
16 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Samira Pouyanfar | 1 | 141 | 13.06 |
| Shu-Ching Chen | 2 | 1978 | 182.74 |