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Analysis of multiobjective algorithms for the classification of multi-label video datasets

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dc.contributor.author Dökeroğlu, Tansel
dc.contributor.author Karagöz, Gizem Nur
dc.contributor.author Yazıcı, Adnan
dc.contributor.author Coşar, Ahmet
dc.date.accessioned 2021-01-13T11:24:15Z
dc.date.available 2021-01-13T11:24:15Z
dc.date.issued 2020
dc.identifier.issn 2169-3536
dc.identifier.uri https://doi.org/10.1109/ACCESS.2020.3022317
dc.identifier.uri http://hdl.handle.net/20.500.12485/722
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 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA en_US
dc.subject Computer Science en_US
dc.subject Information Systems en_US
dc.subject Engineering, Electrical & Electronic en_US
dc.subject Telecommunications en_US
dc.title Analysis of multiobjective algorithms for the classification of multi-label video datasets en_US
dc.type Article en_US
dc.relation.journal IEEE ACCESS en_US
dc.identifier.startpage 163937 en_US
dc.identifier.endpage 163952 en_US
dc.identifier.volume 8 en_US


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