A new framework of multi-objective evolutionary algorithms for feature selection and multi-label classification of video data
Karagoz G.N. Yazici A. Dokeroglu T. Cosar A.
January 2021Springer Science and Business Media Deutschland GmbH
International Journal of Machine Learning and Cybernetics
2021#12Issue 153 - 71 pp.
There are few studies in the literature to address the multi-objective multi-label feature selection for the classification of video data using evolutionary algorithms. Selecting the most appropriate subset of features is a significant problem while maintaining/improving the accuracy of the prediction results. This study proposes a framework of parallel multi-objective Non-dominated Sorting Genetic Algorithms (NSGA-II) for exploring a Pareto set of non-dominated solutions. The subsets of non-dominated features are extracted and validated by multi-label classification techniques, Binary Relevance (BR), Classifier Chains (CC), Pruned Sets (PS), and Random k-Labelset (RAkEL). Base classifiers such as Support Vector Machines (SVM), J48-Decision Tree (J48), and Logistic Regression (LR) are performed in the classification phase of the algorithms. Comprehensive experiments are carried out with local feature descriptors extracted from two multi-label data sets, the well-known MIR-Flickr dataset and a Wireless Multimedia Sensor (WMS) dataset that we have generated from our video recordings. The prediction accuracy levels are improved by 6.36% and 25.7% for the MIR-Flickr and WMS datasets respectively while the number of features is significantly reduced. The results verify that the algorithms presented in this new framework outperform the state-of-the-art algorithms.
Evolutionary , Feature selection , Machine learning , Multi-label classification , Multi-objective optimization
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Department of Computer Engineering, Middle East Technical University, Ankara, Turkey
Department of Computer Science, Nazarbayev University, Nur-Sultan, Kazakhstan
Department of Computer Engineering, TED University, Ankara, Turkey
Department of Computer Engineering, Ankara Bilim University, Ankara, Turkey
Department of Computer Engineering
Department of Computer Science
Department of Computer Engineering
Department of Computer Engineering
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