A Multi-Label Classification With Hybrid Label-Based Meta-Learning Method in Internet of Things
Sung-Chiang Lin, Chih‐Jou Chen, Tsung-Ju Lee
Abstract
With the widespread adoption of Internet connected devices and the application of Internet of Things (IoT), more and more research efforts focusing on using machine learning techniques in recognizing activities from IoT sensors, especially in solving multi-label classification problems. Without considering the associations among labels, traditional approaches aim to transform the original multi-label classification problem into several single-label classification problems. The loss of information among labels will damage the classification performance. In this paper, we proposed a novel hybrid label-based meta-learning algorithm for multi-label classification based on an ensemble of a cluster algorithm and generalized linear mixed model (GLMM). In this algorithm, the clustering phase is performed to catch the association among labels and to reduce the computational complexity from vast label subsets simultaneously, and the GLMM phase is performed to solve dependence of a subject with multi-labels in training data. The numerical results show that the proposed algorithm outperforms others, especially for cases with relatively large number of labels.