<p>Missing data significantly impacts Internet of Things (IoT) applications, causing various issues depending on the type and amount of the missing information. For example, in wearable device applications, missing acceleration readings can result in misclassification of physical activities. Handling missing data in IoT applications, particularly for tasks such as human activity classification, is a complex challenge. In this paper, we propose a two-stage approach for IoT data classification that handles missing data effectively. In the first stage, we build an ensemble of heterogeneous classifiers, while in the second stage, a meta-learner is employed to address high levels of data sparsity without resorting to data imputation or reconstruction methods. The meta-learner is trained on a dataset that combines the original features with the predictions from the first-stage classifiers. By leveraging both feature analysis and classifier performance, the meta-learner produces accurate predictions, ensuring robust results even in the presence of missing data. We address three mechanisms for handling incomplete data: (i) missing at random, (ii) missing completely at random, and (iii) missing not at random, each with various levels of missing data. Our results demonstrate the viability of the proposed approach in handling extreme levels of missing data in both training and testing datasets, consistently outperforming state-of-the-art models.</p>