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Analysis of multiobjective algorithms for the classification of multi-label video datasets
2020
IEEE Access
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
doi:10.1109/access.2020.3022317
fatcat:tfpr76ndjvbkdbyj4h7etdhyli