In the online data stream environment, a model is typically not effective until a sufficient number of data instances have been seen. One solution to alleviate this issue is using model transfer. A major gap in current research is that they do not address the cost-effectiveness of model transfer in the online context, where processing time is crucial as data instances continuously arrive at high-speed. Model transfer in data streams involves continuously adapting the model to new data in the target stream. Suppose the target stream contains hard-to-learn patterns not covered by the transferred model. In that case, adaptation may incur extra processing time with little accuracy performance gains compared to simply building a new model for the target stream from scratch. Therefore, considering the cost of model transfer and target model construction is essential to balance the accuracy performance gains against computation cost for cost-effective model transfer. To address this gap, we propose a framework called OPERA (Online Transfer using Phantom Tree for Real-Time Adaptation), that orchestrates transfer learning based on the accuracy gain and runtime tradeoffs between transferring and adapting a source model, versus no transfer and constructing a new model for the target stream. We conduct extensive empirical studies to show that our framework can balance accuracy performance gains and runtime for cost-effective transfer learning in the data stream environment.

In many real-world applications, data are often produced in the form of streams. Consider, for example, data produced by sensors. In data streams there can be concept drift where the distribution of the data changes. When we deal with multiple streams from the same domain, concepts that have occurred in one stream may occur in another. Therefore, being able to reuse knowledge across multiple streams can help models recover from concept drifts more quickly. A major challenge is that these data streams may be only partially identical and a direct adoption would not suffice. In this paper, we propose a novel framework to transfer both identical and partially identical concepts across different streams. In particular, we propose a new technique called Adaptive Online TrAdaBoost that tunes weight adjustments during boosting based on model performance. The experiments on synthetic data verify the desired properties of the proposed method, and the experiments on real-world data show the better performance of the method for data stream mining compared with its baselines.

Most machine learning algorithms rely on a set of hyperparameters, which are tuned either by domain experts or using automated methods. In the context of data streams, the complexity of this task is compounded by the need for tuning every time a concept drift occurs. The problem of automated tuning may lead to classification performance improvements. Current methods optimise the hyperparameters from scratch every time concept drift is detected. Additionally, meta-learning techniques developed for static machine learning tasks allow pre-built knowledge to adapt faster to new tasks. We propose a meta-learning approach for automated tuning in evolving data streams, specifically for tuning parameters in Adaptive Random Forest (ARF) and parameters for several drift detectors. We compare our approach against non-adaptive methods on both synthetic and real-world datasets. Empirical results show that our approach makes a compromises between predictive performance and resource consumption for ARF, and between true positives and false positives for drift detectors.

Concept drift detection is used to signal to a learning algorithm that there has been a change in the underlying distribution of the data stream. However, there is a delay in detecting the actual drifts, leading to performance loss between the start of the drift and the detection point. There are two major challenges in reducing such performance loss, specifically the difficulty in anticipating the location of the next drift point and determining the exact concept that will appear for timely concept adaptation. In this research, we leverage concept recurrences in data streams. We proposed a framework called Nacre, which can perform proactive drift detection and online updates to allow for smooth adaptation of concept drifts. We present a novel technique, called drift coordinator, that anticipates the next drift point and assesses the incoming concept. This will ultimately increase accuracy in the classification performance. We demonstrate that our method is able to learn and predict drift trends in streams with recurring drifts. This allows the anticipation of future changes which enables users and detection methods to be more proactive. We empirically show that our technique outperforms baselines in terms of accuracy, kappa, accuracy gain per drift and cumulative accuracy gain on both synthetic and real-world datasets.