32 research outputs found
Concept drift detection based on anomaly analysis
© Springer International Publishing Switzerland 2014. In online machine learning, the ability to adapt to new concept quickly is highly desired. In this paper, we propose a novel concept drift detection method, which is called Anomaly Analysis Drift Detection (AADD), to improve the performance of machine learning algorithms under non-stationary environment. The proposed AADD method is based on an anomaly analysis of learner’s accuracy associate with the similarity between learners’ training domain and test data. This method first identifies whether there are conflicts between current concept and new coming data. Then the learner will incrementally learn the non conflict data, which will not decrease the accuracy of the learner on previous trained data, for concept extension. Otherwise, a new learner will be created based on the new data. Experiments illustrate that this AADD method can detect new concept quickly and learn extensional drift incrementally
Frouros: A Python library for drift detection in machine learning systems
Frouros is an open-source Python library capable of detecting drift in
machine learning systems. It provides a combination of classical and more
recent algorithms for drift detection: both concept and data drift. We have
designed it with the objective of making it compatible with any machine
learning framework and easily adaptable to real-world use cases. The library is
developed following a set of best development and continuous integration
practices to ensure ease of maintenance and extensibility. The source code is
available at https://github.com/IFCA/frouros.Comment: 11 pages, 1 tabl
Detecting change via competence model
In real world applications, interested concepts are more likely to change rather than remain stable, which is known as concept drift. This situation causes problems on predictions for many learning algorithms including case-base reasoning (CBR). When learning under concept drift, a critical issue is to identify and determine "when" and "how" the concept changes. In this paper, we developed a competence-based empirical distance between case chunks and then proposed a change detection method based on it. As a main contribution of our work, the change detection method provides an approach to measure the distribution change of cases of an infinite domain through finite samples and requires no prior knowledge about the case distribution, which makes it more practical in real world applications. Also, different from many other change detection methods, we not only detect the change of concepts but also quantify and describe this change. © 2010 Springer-Verlag
Tracking changes using Kullback-Leibler divergence for the continual learning
Recently, continual learning has received a lot of attention. One of the
significant problems is the occurrence of \emph{concept drift}, which consists
of changing probabilistic characteristics of the incoming data. In the case of
the classification task, this phenomenon destabilizes the model's performance
and negatively affects the achieved prediction quality. Most current methods
apply statistical learning and similarity analysis over the raw data. However,
similarity analysis in streaming data remains a complex problem due to time
limitation, non-precise values, fast decision speed, scalability, etc. This
article introduces a novel method for monitoring changes in the probabilistic
distribution of multi-dimensional data streams. As a measure of the rapidity of
changes, we analyze the popular Kullback-Leibler divergence. During the
experimental study, we show how to use this metric to predict the concept drift
occurrence and understand its nature. The obtained results encourage further
work on the proposed methods and its application in the real tasks where the
prediction of the future appearance of concept drift plays a crucial role, such
as predictive maintenance.Comment: Accepted manuscript in SMC 2022, it will be published in the IEEE
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Reservoir of Diverse Adaptive Learners and Stacking Fast Hoeffding Drift Detection Methods for Evolving Data Streams
The last decade has seen a surge of interest in adaptive learning algorithms
for data stream classification, with applications ranging from predicting ozone
level peaks, learning stock market indicators, to detecting computer security
violations. In addition, a number of methods have been developed to detect
concept drifts in these streams. Consider a scenario where we have a number of
classifiers with diverse learning styles and different drift detectors.
Intuitively, the current 'best' (classifier, detector) pair is application
dependent and may change as a result of the stream evolution. Our research
builds on this observation. We introduce the \mbox{Tornado} framework that
implements a reservoir of diverse classifiers, together with a variety of drift
detection algorithms. In our framework, all (classifier, detector) pairs
proceed, in parallel, to construct models against the evolving data streams. At
any point in time, we select the pair which currently yields the best
performance. We further incorporate two novel stacking-based drift detection
methods, namely the \mbox{FHDDMS} and \mbox{FHDDMS}_{add} approaches. The
experimental evaluation confirms that the current 'best' (classifier, detector)
pair is not only heavily dependent on the characteristics of the stream, but
also that this selection evolves as the stream flows. Further, our
\mbox{FHDDMS} variants detect concept drifts accurately in a timely fashion
while outperforming the state-of-the-art.Comment: 42 pages, and 14 figure
Drift Detection using Uncertainty Distribution Divergence
Data generated from naturally occurring processes tends to be non-stationary. For example, seasonal and gradual changes in climate data and sudden changes in financial data. In machine learning the degradation in classifier performance due to such changes in the data is known as concept drift and there are many approaches to detecting and handling it.
Most approaches to detecting concept drift, however, make the assumption that true classes for test examples will be available at no cost shortly after classification and base the detection of concept drift on measures relying on these labels. The high labelling cost in many domains provides a strong motivation to reduce the number of labelled instances required to detect and handle concept drift.
Triggered detection approaches that do not require labelled instances to detect concept drift show great promise for achieving this. In this paper we present Confidence Distribution Batch Detection (CDBD), an approach that provides a signal correlated to changes in concept without using labelled data. This signal combined with a trigger and a rebuild policy can maintain classifier accuracy which, in most cases, matches the accuracy achieved using classification error based detection techniques but using only a limited amount of labelled data
Designing monitoring strategies for deployed machine learning algorithms: navigating performativity through a causal lens
After a machine learning (ML)-based system is deployed, monitoring its
performance is important to ensure the safety and effectiveness of the
algorithm over time. When an ML algorithm interacts with its environment, the
algorithm can affect the data-generating mechanism and be a major source of
bias when evaluating its standalone performance, an issue known as
performativity. Although prior work has shown how to validate models in the
presence of performativity using causal inference techniques, there has been
little work on how to monitor models in the presence of performativity. Unlike
the setting of model validation, there is much less agreement on which
performance metrics to monitor. Different monitoring criteria impact how
interpretable the resulting test statistic is, what assumptions are needed for
identifiability, and the speed of detection. When this choice is further
coupled with the decision to use observational versus interventional data, ML
deployment teams are faced with a multitude of monitoring options. The aim of
this work is to highlight the relatively under-appreciated complexity of
designing a monitoring strategy and how causal reasoning can provide a
systematic framework for choosing between these options. As a motivating
example, we consider an ML-based risk prediction algorithm for predicting
unplanned readmissions. Bringing together tools from causal inference and
statistical process control, we consider six monitoring procedures (three
candidate monitoring criteria and two data sources) and investigate their
operating characteristics in simulation studies. Results from this case study
emphasize the seemingly simple (and obvious) fact that not all monitoring
systems are created equal, which has real-world impacts on the design and
documentation of ML monitoring systems