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Localization of Concept Drift: Identifying the Drifting Datapoints
2022 International Joint Conference on Neural Networks (IJCNN), 2022The notion of concept drift refers to the phenomenon that the distribution which is underlying the observed data changes over time. As a consequence machine learning models may become inaccurate and need adjustment. While there do exist methods to detect concept drift, to find change points in data streams, or to adjust models in the presence of ...
Hinder, Fabian +4 more
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Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '03, 2003
We describe a visualization technique that uses brushed, parallel histograms to aid in understanding concept drift in multidimensional problem spaces. This technique illustrates the relationship between changes in distributions of multiple antecedent feature values and the outcome distribution. We can also observe effects on the relative utilization of
Kevin B. Pratt, Gleb Tschapek
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We describe a visualization technique that uses brushed, parallel histograms to aid in understanding concept drift in multidimensional problem spaces. This technique illustrates the relationship between changes in distributions of multiple antecedent feature values and the outcome distribution. We can also observe effects on the relative utilization of
Kevin B. Pratt, Gleb Tschapek
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Concept Drift and How to Identify It
SSRN Electronic Journal, 2011This paper studies concept drift over time. We first define the meaning of a concept in terms of intension, extension and label. Then we study concept drift over time using two theories: one based on concept identity and one based on concept morphing.
Shenghui Wang 0001 +2 more
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Adaptation to Drifting Concepts
2003Most of supervised learning algorithms assume the stability of the target concept over time. Nevertheless in many real-user modeling systems, where the data is collected over an extended period of time, the learning task can be complicated by changes in the distribution underlying the data.
Gladys Castillo +2 more
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Paired Learners for Concept Drift
2008 Eighth IEEE International Conference on Data Mining, 2008To cope with concept drift, we paired a stable online learner with a reactive one. A stable learner predicts based on all of its experience, whereas are active learner predicts based on its experience over a short, recent window of time. The method of paired learning uses differences in accuracy between the two learners over this window to determine ...
Stephen H. Bach, Marcus A. Maloof
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Detecting and adapting to drifting concepts
2012 9th International Conference on Fuzzy Systems and Knowledge Discovery, 2012The importance of incremental learning in changing environments has been acknowledged in recent years. In this paper we present an ensemble learning method for supervised learning with drifting concepts. The method employs hypothesis test as mechanism for detecting concept drift and learns a base classifier for each new training data chunk.
Haixia Chen, Shengxian Ma, Kai Jiang
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Dealing With Concept Drifts in Process Mining
IEEE Transactions on Neural Networks and Learning Systems, 2014Although most business processes change over time, contemporary process mining techniques tend to analyze these processes as if they are in a steady state. Processes may change suddenly or gradually. The drift may be periodic (e.g., because of seasonal influences) or one-of-a-kind (e.g., the effects of new legislation).
R. P. Jagadeesh Chandra Bose +3 more
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2021
Most extant machine learning strategies focus on learning to make predictions in environments that assume concepts never change. This thesis studies how algorithms can make effective predictions in a changing world where processes of interest are constantly evolving.
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Most extant machine learning strategies focus on learning to make predictions in environments that assume concepts never change. This thesis studies how algorithms can make effective predictions in a changing world where processes of interest are constantly evolving.
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Clustering in the Presence of Concept Drift
2019Clustering naturally addresses many of the challenges of data streams and many data stream clustering algorithms (DSCAs) have been proposed. The literature does not, however, provide quantitative descriptions of how these algorithms behave in different circumstances.
Richard Hugh Moulton +3 more
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