When constructing a classifier ensemble, diversity among the base classifiers is one of the important characteristics. Several studies have been made in the context of standard static data, in particular, when analyzing the relationship between a high ensemble predictive performance and the diversity of its components. Besides, ensembles of learning machines have been performed to learn in the presence of concept drift and adapt to it. However, diversity measures have not received much research interest in evolving data streams. Only a few researchers directly consider promoting diversity while constructing an ensemble or rebuilding them in the moment of detecting drifts. In this paper, we present a theoretical analysis of different diversity measures and relate them to the success of ensemble learning algorithms for streaming data. The analysis provides a deeper understanding of the concept of diversity and its impact on online ensemble Learning in the presence of concept drift. More precisely, we are interested in answering the following research question; Which commonly used diversity measures are used in the context of static-data ensembles and how far are they applicable in the context of streaming data ensembles?
In this work we present classifier patching, an approach for adapting an existing black-box classification model to new data. Instead of creating a new model, patching infers regions in the instance space where the existing model is error-prone by training a classifier on the previously misclassified data. It then learns a specific model to determine the error regions, which allows to patch the old model’s predictions for them. Patching relies on a strong, albeit unchangeable, existing base classifier, and the idea that the true labels of seen instances will be available in batches at some point in time after the original classification. We experimentally evaluate our approach, and show that it meets the original design goals. Moreover, we compare our approach to existing methods from the domain of ensemble stream classification in both concept drift and transfer learning situations. Patching adapts quickly and achieves high classification accuracy, outperforming state-of-the-art competitors in either adaptation speed or accuracy in many scenarios.
In a streaming environment, there is often a need for statistical prediction models to detect and adapt to concept drifts (i.e., changes in the joint distribution between predictor and response variables) so as to mitigate deteriorating predictive performance over time. Various concept drift detection approaches have been proposed in the past decades. However, they do not perform well across different concept drift types (e.g., gradual or abrupt, recurrent or irregular) and different data stream distributions (e.g., balanced and imbalanced labels). This paper presents a novel framework that can detect and also adapt to the various concept drift types, even in the presence of imbalanced data labels. The framework leverages a hierarchical set of hypothesis tests in an online fashion to detect concept drifts and employs an adaptive training strategy to significantly boost its adaptation capability. The performance of the proposed framework is compared to benchmark approaches using both simulated and real-world datasets spanning the breadth of concept drift types. The proposed approach significantly outperforms benchmark solutions in terms of precision, delay of detection as well as the adaptability across different concepts.
Classifiers deployed in the real world operate in a dynamic environment, where the data distribution can change over time. These changes, referred to as concept drift, can cause the predictive performance of the classifier to drop over time, thereby making it obsolete. To be of any real use, these classifiers need to detect drifts and be able to adapt to them, over time. Detecting drifts has traditionally been approached as a supervised task, with labeled data constantly being used for validating the learned model. Although effective in detecting drifts, these techniques are impractical, as labeling is a difficult, costly and time consuming activity. On the other hand, unsupervised change detection techniques are unreliable, as they produce a large number of false alarms. The inefficacy of the unsupervised techniques stems from the exclusion of the characteristics of the learned classifier, from the detection process. In this paper, we propose the Margin Density Drift Detection (MD3) algorithm, which tracks the number of samples in the uncertainty region of a classifier, as a metric to detect drift. The MD3 algorithm is a distribution independent, application independent, model independent, unsupervised and incremental algorithm for reliably detecting drifts from data streams. Experimental evaluation on 6 drift induced datasets and 4 additional datasets from the cybersecurity domain demonstrates that the MD3 approach can reliably detect drifts, with significantly fewer false alarms compared to unsupervised feature based drift detectors. The reduced false alarms enables the signaling of drifts only when they are most likely to affect classification performance. As such, the MD3 approach leads to a detection scheme which is credible, label efficient and general in its applicability.
Haque, Ahsanul (University of Texas at Dallas) | Tao, Hemeng (University of Texas at Dallas) | Chandra, Swarup (University of Texas at Dallas) | Liu, Jie (University of Texas at Dallas ) | Khan, Latifur (University of Computer Science at Dallas)
Regression over a stream of data is challenging due to unbounded data size and non-stationary distribution over time. Typically, a traditional supervised regression model over a data stream is trained on data instances occurring within a short time period by assuming a stationary distribution. This model is later used to predict value of response-variable in future instances. Over time, the model may degrade in performance due to changes in data distribution among incoming data instances. Updating the model for change adaptation requires true value for every recent data instances, which is scarce in practice. To overcome this issue, recent studies have employed techniques that sample fewer instances to be used for model retraining. Yet, this may introduce sampling bias that adversely affects the model performance. In this paper, we study the regression problem over data streams in a novel setting. We consider two independent, yet related, non-stationary data streams, which are referred to as the source and the target stream. The target stream continuously generates data instances whose value of response variable is unknown. The source stream, however, continuously generates data instances along with corresponding value for the response-variable, and has a biased data distribution with respect to the target stream. We refer to the problem of using a model trained on the biased source stream to predict the response-variable’s value in data instances occurring on the target stream as Multistream Regression. In this paper, we describe a framework for multistream regression that simultaneously overcomes distribution bias and detects change in data distribution represented by the two streams over time using a Gaussian kernel model. We analyze the theoretical properties of the proposed approach and empirically evaluate it on both real-world and synthetic data sets. Importantly, our results indicate superior performance by the framework compared to other baseline regression methods.