An Empirical Study of Uncertainty Estimation Techniques for Detecting Drift in Data Streams

In safety-critical domains such as autonomous driving and medical diagnosis, the reliability of machine learning models is crucial. One significant challenge to reliability is concept drift, which can cause model deterioration over time. Traditionally, drift detectors rely on true labels, which are often scarce and costly. This study conducts a comprehensive empirical evaluation of using uncertainty values as substitutes for error rates in detecting drifts, aiming to alleviate the reliance on labeled post-deployment data. We examine five uncertainty estimation methods in conjunction with the ADWIN detector across seven real-world datasets. Our results reveal that while the SWAG method exhibits superior calibration, the overall accuracy in detecting drifts is not notably impacted by the choice of uncertainty estimation method, with even the most basic method demonstrating competitive performance. These findings offer valuable insights into the practical applicability of uncertainty-based drift detection in real-world, safety-critical applications.Comment: NeurIPS 2023: Workshop on Distribution Shift

A Survey on Concept Drift Adaptation

Concept drift primarily refers to an online supervised learning scenario when the relation between the in- put data and the target variable changes over time. Assuming a general knowledge of supervised learning in this paper we characterize adaptive learning process, categorize existing strategies for handling concept drift, discuss the most representative, distinct and popular techniques and algorithms, discuss evaluation methodology of adaptive algorithms, and present a set of illustrative applications. This introduction to the concept drift adaptation presents the state of the art techniques and a collection of benchmarks for re- searchers, industry analysts and practitioners. The survey aims at covering the different facets of concept drift in an integrated way to reflect on the existing scattered state-of-the-art

Solving the challenges of concept drift in data stream classification.

The rise of network connected devices and applications leads to a significant increase in the volume of data that are continuously generated overtime time, called data streams. In real world applications, storing the entirety of a data stream for analyzing later is often not practical, due to the data stream’s potentially infinite volume. Data stream mining techniques and frameworks are therefore created to analyze streaming data as they arrive. However, compared to traditional data mining techniques, challenges unique to data stream mining also emerge, due to the high arrival rate of data streams and their dynamic nature. In this dissertation, an array of techniques and frameworks are presented to improve the solutions on some of the challenges. First, this dissertation acknowledges that a “no free lunch” theorem exists for data stream mining, where no silver bullet solution can solve all problems of data stream mining. The dissertation focuses on detection of changes of data distribution in data stream mining. These changes are called concept drift. Concept drift can be categorized into many types. A detection algorithm often works only on some types of drift, but not all of them. Because of this, the dissertation finds specific techniques to solve specific challenges, instead of looking for a general solution. Then, this dissertation considers improving solutions for the challenges of high arrival rate of data streams. Data stream mining frameworks often need to process vast among of data samples in limited time. Some data mining activities, notably data sample labeling for classification, are too costly or too slow in such large scale. This dissertation presents two techniques that reduce the amount of labeling needed for data stream classification. The first technique presents a grid-based label selection process that apply to highly imbalanced data streams. Such data streams have one class of data samples vastly outnumber another class. Many majority class samples need to be labeled before a minority class sample can be found due to the imbalance. The presented technique divides the data samples into groups, called grids, and actively search for minority class samples that are close by within a grid. Experiment results show the technique can reduce the total number of data samples needed to be labeled. The second technique presents a smart preprocessing technique that reduce the number of times a new learning model needs to be trained due to concept drift. Less model training means less data labels required, and thus costs less. Experiment results show that in some cases the reduced performance of learning models is the result of improper preprocessing of the data, not due to concept drift. By adapting preprocessing to the changes in data streams, models can retain high performance without retraining. Acknowledging the high cost of labeling, the dissertation then considers the scenario where labels are unavailable when needed. The framework Sliding Reservoir Approach for Delayed Labeling (SRADL) is presented to explore solutions to such problem. SRADL tries to solve the delayed labeling problem where concept drift occurs, and no labels are immediately available. SRADL uses semi-supervised learning by employing a sliding windowed approach to store historical data, which is combined with newly unlabeled data to train new models. Experiments show that SRADL perform well in some cases of delayed labeling. Next, the dissertation considers improving solutions for the challenge of dynamism within data streams, most notably concept drift. The complex nature of concept drift means that most existing detection algorithms can only detect limited types of concept drift. To detect more types of concept drift, an ensemble approach that employs various algorithms, called Heuristic Ensemble Framework for Concept Drift Detection (HEFDD), is presented. The occurrence of each type of concept drift is voted on by the detection results of each algorithm in the ensemble. Types of concept drift with votes past majority are then declared detected. Experiment results show that HEFDD is able to improve detection accuracy significantly while reducing false positives. With the ability to detect various types of concept drift provided by HEFDD, the dissertation tries to improve the delayed labeling framework SRADL. A new combined framework, SRADL-HEFDD is presented, which produces synthetic labels to handle the unavailability of labels by human expert. SRADL-HEFDD employs different synthetic labeling techniques based on different types of drift detected by HEFDD. Experimental results show that comparing to the default SRADL, the combined framework improves prediction performance when small amount of labeled samples is available. Finally, as machine learning applications are increasingly used in critical domains such as medical diagnostics, accountability, explainability and interpretability of machine learning algorithms needs to be considered. Explainable machine learning aims to use a white box approach for data analytics, which enables learning models to be explained and interpreted by human users. However, few studies have been done on explaining what has changed in a dynamic data stream environment. This dissertation thus presents Data Stream Explainability (DSE) framework. DSE visualizes changes in data distribution and model classification boundaries between chunks of streaming data. The visualizations can then be used by a data mining researcher to generate explanations of what has changed within the data stream. To show that DSE can help average users understand data stream mining better, a survey was conducted with an expert group and a non-expert group of users. Results show DSE can reduce the gap of understanding what changed in data stream mining between the two groups

Detecting Concept Drift With Neural Network Model Uncertainty

Deployed machine learning models are confronted with the problem of changing data over time, a phenomenon also called concept drift. While existing approaches of concept drift detection already show convincing results, they require true labels as a prerequisite for successful drift detection. Especially in many real-world application scenarios-like the ones covered in this work-true labels are scarce, and their acquisition is expensive. Therefore, we introduce a new algorithm for drift detection, Uncertainty Drift Detection (UDD), which is able to detect drifts without access to true labels. Our approach is based on the uncertainty estimates provided by a deep neural network in combination with Monte Carlo Dropout. Structural changes over time are detected by applying the ADWIN technique on the uncertainty estimates, and detected drifts trigger a retraining of the prediction model. In contrast to input data-based drift detection, our approach considers the effects of the current input data on the properties of the prediction model rather than detecting change on the input data only (which can lead to unnecessary retrainings). We show that UDD outperforms other state-of-the-art strategies on two synthetic as well as ten real-world data sets for both regression and classification tasks

A survey on feature drift adaptation: Definition, benchmark, challenges and future directions

Data stream mining is a fast growing research topic due to the ubiquity of data in several real-world problems. Given their ephemeral nature, data stream sources are expected to undergo changes in data distribution, a phenomenon called concept drift. This paper focuses on one specific type of drift that has not yet been thoroughly studied, namely feature drift. Feature drift occurs whenever a subset of features becomes, or ceases to be, relevant to the learning task; thus, learners must detect and adapt to these changes accordingly. We survey existing work on feature drift adaptation with both explicit and implicit approaches. Additionally, we benchmark several algorithms and a naive feature drift detection approach using synthetic and real-world datasets. The results from our experiments indicate the need for future research in this area as even naive approaches produced gains in accuracy while reducing resources usage. Finally, we state current research topics, challenges and future directions for feature drift adaptation

A survey on online active learning

Online active learning is a paradigm in machine learning that aims to select the most informative data points to label from a data stream. The problem of minimizing the cost associated with collecting labeled observations has gained a lot of attention in recent years, particularly in real-world applications where data is only available in an unlabeled form. Annotating each observation can be time-consuming and costly, making it difficult to obtain large amounts of labeled data. To overcome this issue, many active learning strategies have been proposed in the last decades, aiming to select the most informative observations for labeling in order to improve the performance of machine learning models. These approaches can be broadly divided into two categories: static pool-based and stream-based active learning. Pool-based active learning involves selecting a subset of observations from a closed pool of unlabeled data, and it has been the focus of many surveys and literature reviews. However, the growing availability of data streams has led to an increase in the number of approaches that focus on online active learning, which involves continuously selecting and labeling observations as they arrive in a stream. This work aims to provide an overview of the most recently proposed approaches for selecting the most informative observations from data streams in the context of online active learning. We review the various techniques that have been proposed and discuss their strengths and limitations, as well as the challenges and opportunities that exist in this area of research. Our review aims to provide a comprehensive and up-to-date overview of the field and to highlight directions for future work

Continual learning from stationary and non-stationary data

Continual learning aims at developing models that are capable of working on constantly evolving problems over a long-time horizon. In such environments, we can distinguish three essential aspects of training and maintaining machine learning models - incorporating new knowledge, retaining it and reacting to changes. Each of them poses its own challenges, constituting a compound problem with multiple goals. Remembering previously incorporated concepts is the main property of a model that is required when dealing with stationary distributions. In non-stationary environments, models should be capable of selectively forgetting outdated decision boundaries and adapting to new concepts. Finally, a significant difficulty can be found in combining these two abilities within a single learning algorithm, since, in such scenarios, we have to balance remembering and forgetting instead of focusing only on one aspect. The presented dissertation addressed these problems in an exploratory way. Its main goal was to grasp the continual learning paradigm as a whole, analyze its different branches and tackle identified issues covering various aspects of learning from sequentially incoming data. By doing so, this work not only filled several gaps in the current continual learning research but also emphasized the complexity and diversity of challenges existing in this domain. Comprehensive experiments conducted for all of the presented contributions have demonstrated their effectiveness and substantiated the validity of the stated claims