Missing label imputation through inception-based semi-supervised ensemble learning

In classification tasks, unlabeled data bring the uncertainty in the learning process, which may result in the degradation of the performance. In this paper, we propose a novel semi-supervised inception neural network ensemble-based architecture to achieve missing label imputation. The main idea of the proposed architecture is to use smaller ensembles within a larger ensemble to involve diverse ways of missing label imputation and internal transformation of feature representation, towards enhancing the prediction accuracy. Following the process of imputing the missing labels of unlabeled data, the human-labeled data and the data with imputed labels are used together as a training set for the credible classifiers learning. Meanwhile, we discuss how this proposed approach is more effective as compared to the traditional ensemble learning approaches. Our proposed approach is evaluated on different well-known benchmark data sets, and the experimental results show the effectiveness of the proposed method. In addition, the approach is validated by statistical analysis using Wilcoxon signed rank test and the results indicate statistical significance of the performance improvement in comparison with other methods.