Just-In-Time Classifiers for Recurrent Concepts

Alippi, Cesare; Boracchi, Giacomo; Roveri, Manuel

doi:10.1109/tnnls.2013.2239309

Cited by 135 publications

(89 citation statements)

References 31 publications

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“…Additionally, most of the shift-detection methods present in the literature are based on the batch processing for a dataset shift detection (Gama and Kosina 2014;Alippi et al 2013;Elwell and Polikar 2011;, so there is a time delay in shift-detection. Hence, for real-time systems, the batch processing methods are not beneficial where initiating adaptation in the nick-of-time is of supreme interest.…”

Section: Introductionmentioning

confidence: 99%

Adaptive learning with covariate shift-detection for motor imagery-based brain–computer interface

Raza

Cecotti

et al. 2015

Soft Comput

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A common assumption in traditional supervised learning is the similar probability distribution of data between the training phase and the testing/operating phase. When transitioning from the training to testing phase, a shift in the probability distribution of input data is known as a covariate shift. Covariate shifts commonly arise in a wide range of real-world systems such as electroencephalogram-based brain-computer interfaces (BCIs). In such systems, there is a necessity for continuous monitoring of the process behavior, and tracking the state of the covariate shifts to decide about initiating adaptation in a timely manner. This paper presents a covariate shift-detection and -adaptation methodology, and its application to motor imagery-based BCIs. A covariate shift-detection test based on an exponential weighted moving average model is used to detect the covariate shift in the features extracted from motor imagery-based brain responses. Following the covariate shift-detection test, the methodology initiates an adaptation by updating the classifier during the testing/operating phase. The usefulness of the proposed method is evaluated using real-world BCI datasets (i.e. BCI competition IV dataset 2A and 2B). The results show a statistically significant improvement in the classification accuracy of the BCI system over traditional learning and semi-supervised learning methods.

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Section: Introductionmentioning

confidence: 99%

Adaptive learning with covariate shift-detection for motor imagery-based brain–computer interface

Raza

Cecotti

et al. 2015

Soft Comput

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“…The passive style of learning in non-stationary environments is to directly learn an evolving system that is just-in-time adaptive to the presence of changes in the distribution. These adaptive models include both single models [87][88][89] and ensemble models [90,91], but the latter tend to behave more stably and exhibit superior performance.…”

Section: Fig 8 Bias and Non-stationary Puzzles In The Learning Procementioning

confidence: 99%

Addressing Complexities of Machine Learning in Big Data: Principles, Trends and Challenges from Systematical Perspectives

Q¹,

Zhao²,

Huang³

et al. 2017

Preprint

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The concept of 'big data' has been widely discussed, and its value has been illuminated throughout a variety of domains. To quickly mine potential values and alleviate the ever-increasing volume of information, machine learning is playing an increasingly important role and faces more challenges than ever. Because few studies exist regarding how to modify machine learning techniques to accommodate big data environments, we provide a comprehensive overview of the history of the evolution of big data, the foundations of machine learning, and the bottlenecks and trends of machine learning in the big data era. More specifically, based on learning principals, we discuss regularization to enhance generalization. The challenges of quality in big data are reduced to the curse of dimensionality, class imbalances, concept drift and label noise, and the underlying reasons and mainstream methodologies to address these challenges are introduced. Learning model development has been driven by domain specifics, dataset complexities, and the presence or absence of human involvement. In this paper, we propose a robust learning paradigm by aggregating the aforementioned factors. Over the next few decades, we believe that these perspectives will lead to novel ideas and encourage more studies aimed at incorporating knowledge and establishing data-driven learning systems that involve both data quality considerations and human interactions.

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“…5 3 See model building with embedded versus wrapper or filter frameworks for attribute selection [113]. 4 See also 'sample selection bias', Sect. 2.1.…”

Section: 33)mentioning

confidence: 99%

Evolutionary model building under streaming data for classification tasks: opportunities and challenges

Heywood

2014

Genet Program Evolvable Mach

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Streaming data analysis potentially represents a significant shift in emphasis from schemes historically pursued for offline (batch) approaches to the classification task. In particular, a streaming data application implies that: (1) the data itself has no formal 'start' or 'end'; (2) the properties of the process generating the data are non-stationary, thus models that function correctly for some part(s) of a stream may be ineffective elsewhere; (3) constraints on the time to produce a response, potentially implying an anytime operational requirement; and (4) given the prohibitive cost of employing an oracle to label a stream, a finite labelling budget is necessary. The scope of this article is to provide a survey of developments for model building under streaming environments from the perspective of both evolutionary and non-evolutionary frameworks. In doing so, we bring attention to the challenges and opportunities that developing solutions to streaming data classification tasks are likely to face using evolutionary approaches.

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Just-In-Time Classifiers for Recurrent Concepts

Cited by 135 publications

References 31 publications

Adaptive learning with covariate shift-detection for motor imagery-based brain–computer interface

Adaptive learning with covariate shift-detection for motor imagery-based brain–computer interface

Addressing Complexities of Machine Learning in Big Data: Principles, Trends and Challenges from Systematical Perspectives

Evolutionary model building under streaming data for classification tasks: opportunities and challenges

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