A new framework of multi-objective evolutionary algorithms for feature selection and multi-label classification of video data


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.

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This study is supported in part by NU Faculty development competitive research grants program, Nazarbayev University, Grant Number-110119FD4543 and in part by a research grant from TUBITAK (The Scientific and Technological Research Council of Turkey) with the Grant no. 114R082.

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Correspondence to Tansel Dokeroglu.

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Karagoz, G.N., Yazici, A., Dokeroglu, T. et al. A new framework of multi-objective evolutionary algorithms for feature selection and multi-label classification of video data. Int. J. Mach. Learn. & Cyber. 12, 53–71 (2021). https://doi.org/10.1007/s13042-020-01156-w

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  • Multi-label classification
  • Multi-objective optimization
  • Evolutionary
  • Machine learning
  • Feature selection