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

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Abstract

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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Notes

  1. http://ceng.metu.edu.tr/tr/node/3612

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Acknowledgements

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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Authors and Affiliations

  1. Department of Computer Engineering, Middle East Technical University, Ankara, Turkey

    Gizem Nur Karagoz & Adnan Yazici

  2. Department of Computer Science, Nazarbayev University, Nur-Sultan, Kazakhstan

    Adnan Yazici

  3. Department of Computer Engineering, TED University, Ankara, Turkey

    Tansel Dokeroglu

  4. Department of Computer Engineering, Ankara Bilim University, Ankara, Turkey

    Ahmet Cosar

Authors
  1. Gizem Nur Karagoz
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  2. Adnan Yazici
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  3. Tansel Dokeroglu
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  4. Ahmet Cosar
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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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  • DOI: https://doi.org/10.1007/s13042-020-01156-w

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