Article

Online Methods of Learning in Occurrence of Concept Drift

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... This problem can reduce the accuracy of the model because the data may become obsolete quickly over time. There are three types of method that have been successfully applied to solve the concept drift problem as follows: (i) Using a unified framework to detect the drift-an ensemble algorithm to detect using incremental learning manner [11]; (ii) Feature extraction for explicit concept drift detection [12]-using time series features to monitor how concepts evolve over time; and (iii) monitoring the change of error distribution in the learning part [13]. According to [13], detecting the change of error distribution by Drift Detection Method (DDM) is the most efficient way for concept drift detection, but it is only applicable for a classification task. ...
... There are three types of method that have been successfully applied to solve the concept drift problem as follows: (i) Using a unified framework to detect the drift-an ensemble algorithm to detect using incremental learning manner [11]; (ii) Feature extraction for explicit concept drift detection [12]-using time series features to monitor how concepts evolve over time; and (iii) monitoring the change of error distribution in the learning part [13]. According to [13], detecting the change of error distribution by Drift Detection Method (DDM) is the most efficient way for concept drift detection, but it is only applicable for a classification task. In this work, we employ DDM to detect concept drift in the learning part for regression task. ...
... Update the threshold of ALD by (13); 19: ...
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This paper proposes a novel algorithm called Meta-cognitive Recurrent Kernel Online Sequential Extreme Learning Machine with a kernel filter and a modified Drift Detector Mechanism (Meta-RKOS-ELM$$_\mathrm{ALD}$$-DDM). The algorithm aims to tackle a well-known concept drift problem in time series prediction by utilising the modified concept drift detector mechanism. Moreover, the new meta-cognitive learning strategy is employed to solve parameter dependency and reduce learning time. The experimental results show that the proposed method can achieve better performance than the conventional algorithm in a set of financial datasets.
... The information captured by online behavior can thus change over time and lead the model's performance to drop [29]. Although a number of control mechanisms can be put into place (e.g., online learning [29,30]), understanding which behavioral features have a (large) impact on a model's classifications through explanations can help domain experts make sound statements on the expected lifetime of a model and its sensitivity to rapidly changing technological indicators and digital behavior. For example, the type of mobile phone applications that people use might change more rapidly compared to the genres of movies people watch or the type of places they visit on the weekend, which reflect more 'stable' behavior. ...
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Every step we take in the digital world leaves behind a record of our behavior; a digital footprint. Research has suggested that algorithms can translate these digital footprints into accurate estimates of psychological characteristics, including personality traits, mental health or intelligence. The mechanisms by which AI generates these insights, however, often remain opaque. In this paper, we show how Explainable AI (XAI) can help domain experts and data subjects validate, question, and improve models that classify psychological traits from digital footprints. We elaborate on two popular XAI methods (rule extraction and counterfactual explanations) in the context of Big Five personality predictions (traits and facets) from financial transactions data (N = 6,408). First, we demonstrate how global rule extraction sheds light on the spending patterns identified by the model as most predictive for personality, and discuss how these rules can be used to explain, validate, and improve the model. Second, we implement local rule extraction to show that individuals are assigned to personality classes because of their unique financial behavior, and there exists a positive link between the model’s prediction confidence and the number of features that contributed to the prediction. Our experiments highlight the importance of both global and local XAI methods. By better understanding how predictive models work in general as well as how they derive an outcome for a particular person, XAI promotes accountability in a world in which AI impacts the lives of billions of people around the world.
... The information captured by online behavior can thus change over time and lead the model's performance to drop [28]. Although a number of control mechanisms can be put into place (e.g., online learning [28,29]), understanding which behavioral features have a (large) impact on a model's classifications through explanations can help domain experts make sound statements on the expected lifetime of a model and its sensitivity to rapidly changing technological indicators and digital behavior. For example, the type of mobile phone applications that people use might change more rapidly compared to the genres of movies people watch or the type of places they visit on the weekend, which reflect more 'stable' behavior. ...
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Every step we take in the digital world leaves behind a record of our behavior; a digital footprint. Research has suggested that algorithms can translate these digital footprints into accurate estimates of psychological characteristics, including personality traits, mental health or intelligence. The mechanisms by which AI generates these insights, however, often remain opaque. In this paper, we show how Explainable AI (XAI) can help domain experts and data subjects validate, question, and improve models that classify psychological traits from digital footprints. We elaborate on two popular XAI methods (rule extraction and counterfactual explanations) in the context of Big Five personality predictions (traits and facets) from financial transactions data (N = 6,408). First, we demonstrate how global rule extraction sheds light on the spending patterns identified by the model as most predictive for personality, and discuss how these rules can be used to explain, validate, and improve the model. Second, we implement local rule extraction to show that individuals are assigned to personality classes because of their unique financial behavior, and that there exists a positive link between the model's prediction confidence and the number of features that contributed to the prediction. Our experiments highlight the importance of both global and local XAI methods. By better understanding how predictive models work in general as well as how they derive an outcome for a particular person, XAI promotes accountability in a world in which AI impacts the lives of billions of people around the world.
... The stationary data generators generate data continuously with respect to time with uniform data distribution. A data that is generated continuously with time is termed as data stream [2][3][4][5]. ...
... Mittal and Kashyap suggested various online methods of drift detection in his paper. They presented results of experiments and comparison of online drift detection methods [3]. Bifet et al. proposed a new experimental framework for evaluating change detection methods against intended outcomes. ...
... These methods fulfill the one pass requirement of learn ing in data stream without storing the data electronically. The online approaches can be broadly discussed in two categories: (i) Online learning approaches that use an explicit mechanis m to deal with concept drifts [7], [9], [10] and (ii) On line learning approaches that do not use any exp licit mechanism to deal with concept drifts [11][12][13][14][15] [32]. Most popularly, the former online learn ing approaches include Early Drift Detection Method (EDDM) [7] and Drift Detection Method (DDM ) [10]. ...
Article
In the real world, most of the applications are inherently dynamic in nature i.e. their underlying data distribution changes with time. As a result, the concept drifts occur very frequently in the data stream. Concept drifts in data stream increase the challenges in learning as well, it also significantly decreases the accuracy of the classifier. However, recently many algorithms have been proposed that exclusively designed for data stream mining while considering drifting concept in the data stream.This paper presents an empirical evaluation of these algorithms on datasets having four possible types of concept drifts namely; sudden, gradual, incremental, and recurring drifts.
... So, there will be a concept drift between data related to old process and the newer process. In [33], four types of concept drift have been introduced: sudden drifts, gradual drifts, recurring drifts and incremental drifts. In a sudden drift, the whole process will suddenly change from start to end (e.g., due to management changes the whole process changes). ...
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Chapter
Concept drift is the scenario in online learning in which value of target variable changes with respect to time. The learning algorithms should be adaptive in nature in order to cater the changes imposed due to change in concept. This paper discusses about the adaptive algorithms that are used in learning from the evolving data with different changing patterns. The various applications owing concept drift that are major sources of digital data stream and other real-world problems addresses concept drift are also discussed in this paper.
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In the real world concepts are often not stable but change with time. Typical examples of this are weather prediction rules and customers' preferences. The underlying data distribution may change as well. Often these changes make the model built on old data inconsistent with the new data, and regular updating of the model is necessary. This problem, known as concept drift, complicates the task of learning a model from data and requires special approaches, different from commonly used techniques, which treat arriving instances as equally important contributors to the final concept. This paper considers different types of concept drift, peculiarities of the problem, and gives a critical review of existing approaches to the problem.
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DATA STREAM MINING: A Practical Approach
• Albert Bifet
• Richard Kirkby
Albert Bifet and Richard Kirkby, DATA STREAM MINING: A Practical Approach, August 2009.
Learning with drift detection
• J Gama
• P Medas
• G Castillo
• P Rodrigues
Gama, J., Medas, P., Castillo, G. and Rodrigues, P. (2004). Learning with drift detection, Proceedings of the Seventh Brazilian Symposium on Artificial Intelligence (SBIA'04) -Lecture Notes in Computer Science, Vol. 3171, Springer, S˜ao Luiz do Maranh˜ao, Brazil, pp. 286-295.