Analysis of Multiobjective Algorithms for the Classification of Multi-Label Video Datasets

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10.1109:ACCESS.2020.3022317.pdf

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2020

Author

Karagoz, Gizem Nur
Yazıcı, Adnan
Dokeroglu, Tansel
Cosar, Ahmet

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It is of great importance to extract and validate an optimal subset of non-dominated features for effective multi-label classification. However, deciding on the best subset of features is an NP-Hard problem and plays a key role in improving the prediction accuracy and the processing time of video datasets. In this study, we propose autoencoders for dimensionality reduction of video data sets and ensemble the features extracted by the multi-objective evolutionary Non-dominated Sorting Genetic Algorithm and the autoencoder. We explore the performance of well-known multi-label classification algorithms for video datasets in terms of prediction accuracy and the number of features used. More specifically, we evaluate Non-dominated Sorting Genetic Algorithm-II, autoencoders, ensemble learning algorithms, Principal Component Analysis, Information Gain, and Correlation Based Feature Selection. Some of these algorithms use feature selection techniques to improve the accuracy of the classification. Experiments are carried out with local feature descriptors extracted from two multi-label datasets, the MIR-Flickr dataset which consists of images and the Wireless Multimedia Sensor dataset that we have generated from our video recordings. Significant improvements in the accuracy performance of the algorithms are observed while the number of features is being reduced.

Subject Keywords

General Engineering, General Materials Science, General Computer Science, Feature extraction, Prediction algorithms, Optimization, Dimensionality reduction, Machine learning algorithms, Genetic algorithms, Support vector machines, Feature selection, Multi-label, Multi-objective optimization, Autoencoder, Ensemble, Classification

URI

https://hdl.handle.net/11511/51565

Journal

IEEE Access

DOI

https://doi.org/10.1109/access.2020.3022317

Collections

Department of Computer Engineering, Article

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Citation Formats

G. N. Karagoz, A. Yazıcı, T. Dokeroglu, and A. Cosar, “Analysis of Multiobjective Algorithms for the Classification of Multi-Label Video Datasets,” IEEE Access, pp. 163937–163952, 2020, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/51565.