In a non-stationary data stream, concept drift occurs when different chunks of incoming data have different distributions. Hence, over time, the global optimization point of a learning model might permanently drift to the point where the model no longer adequately performs the task it was designed for. This phenomenon needs to be addressed to maintain the integrity and effectiveness of a model over the long term. In this paper, we propose a simple but effective drift learning algorithm called elastic Gradient Boosting Decision Tree (eGBDT). Since the prediction of a GBDT model is the sum output of a list of trees, we can easily append new trees to perform incremental learning or delete the last few trees to roll back to a previously known optimization point. The proposed eGBDT incrementally fits new data and detect drift by searching for the tree with the lowest residual. If the rollback deletions required would exceed the initial number of trees, a retraining process is triggered. Comparisons of eGBDT with five state-of-the-art methods on eight data sets show the efficacy of eGBDT.
Social sustainability, the social pillar of sustainable development, has had increasing influences in recent years. It pursues the realisation of human well‐being and focuses on satisfying human needs and improving the quality of life. Unfortunately, there is a lack of social sustainability research in the aged care sector. The definition and indicators for social sustainability in aged care projects in the Chinese context are unclear, let alone the status of its realisation. This study aimed to establish a social sustainability indicator framework for aged care projects in China. Two focus group meetings and two online Delphi surveys were conducted to finalise and evaluate the indicators. Three stakeholders, 10 social impacts, and 21 indicators were identified. The realisation status of 19 indicators needs to be improved, nine of which need special attention. The indicator framework established in this study will be an effective tool to enhance the understanding and measure the social sustainability of aged care projects. As the world's most populous country, China's experience can provide insights to other countries. Moreover, the findings have implications for understanding the social sustainability of other public service projects with social impact.
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