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

KARAGÖZ G. N., YAZICI A., Dokeroglu T., Cosar A.

INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS, vol.12, no.1, pp.53-71, 2021 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 12 Issue: 1
  • Publication Date: 2021
  • Doi Number: 10.1007/s13042-020-01156-w
  • Journal Name: INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC
  • Page Numbers: pp.53-71
  • Keywords: Multi-label classification, Multi-objective optimization, Evolutionary, Machine learning, Feature selection, GENETIC ALGORITHM, OPTIMIZATION
  • TED University Affiliated: No

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.