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.

文献中很少有研究涉及使用进化算法对视频数据进行分类的多目标多标签特征选择。在维持/提高预测结果的准确性的同时，选择最合适的特征子集是一个重大问题。这项研究提出了一个并行的多目标非支配排序遗传算法（NSGA-II）的框架，用于探索一组Pareto非支配解决方案。通过多标签分类技术，二进制相关性（BR），分类器链（CC），修剪集（PS）和随机k-Labelset（RAkEL）提取和验证非主导特征的子集。在算法的分类阶段中执行基本分类器，例如支持向量机（SVM），J48决策树（J48）和逻辑回归（LR）。我们使用从两个多标签数据集中提取的局部特征描述符（即众所周知的MIR-Flickr数据集和我们从视频记录中生成的无线多媒体传感器（WMS）数据集）进行了全面的实验。MIR-Flickr和WMS数据集的预测准确度分别提高了6.36％和25.7％，而特征数量却大大减少了。结果证明，在此新框架中提出的算法优于最新算法。MIR-Flickr和WMS数据集分别为7％，而特征数量却大大减少了。结果证明，在此新框架中提出的算法优于最新算法。MIR-Flickr和WMS数据集分别为7％，而特征数量却大大减少了。结果证明，在此新框架中提出的算法优于最新算法。