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ANALYSIS OF MULTIOBJECTIVE ALGORITHMS FOR THE CLASSIFICATION OF MULTI-LABEL VIDEO DATASETS

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dc.contributor.author Karagoz, Gizem Nur
dc.contributor.author Yazici, Adnan
dc.contributor.author Dokeroglu, Tansel
dc.contributor.author Cosar, Ahmet
dc.date.accessioned 2021-02-24T05:33:41Z
dc.date.available 2021-02-24T05:33:41Z
dc.date.issued 2020-09-07
dc.identifier.citation Karagoz, G. N., Yazici, A., Dokeroglu, T., & Cosar, A. (2020). Analysis of Multiobjective Algorithms for the Classification of Multi-Label Video Datasets. IEEE Access, 8, 163937–163952. https://doi.org/10.1109/access.2020.3022317 en_US
dc.identifier.issn 2169-3536
dc.identifier.issn https://ieeexplore.ieee.org/document/9187265
dc.identifier.uri https://doi.org/10.1109/ACCESS.2020.3022317
dc.identifier.uri http://nur.nu.edu.kz/handle/123456789/5336
dc.description.abstract 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. en_US
dc.language.iso en en_US
dc.publisher Institute of Electrical and Electronics Engineers en_US
dc.relation.ispartofseries IEEE Access;8, 163937–163952
dc.rights Attribution-NonCommercial-ShareAlike 3.0 United States *
dc.rights.uri http://creativecommons.org/licenses/by-nc-sa/3.0/us/ *
dc.subject Feature selection en_US
dc.subject multi-label en_US
dc.subject multi-objective optimization en_US
dc.subject autoencoder en_US
dc.subject ensemble en_US
dc.subject classification en_US
dc.subject Research Subject Categories::TECHNOLOGY en_US
dc.title ANALYSIS OF MULTIOBJECTIVE ALGORITHMS FOR THE CLASSIFICATION OF MULTI-LABEL VIDEO DATASETS en_US
dc.type Article en_US
workflow.import.source science


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