Stream-based active learning with linear models

Cacciarelli, Davide; Külahçı, Murat; Tyssedal, John

doi:10.1016/j.knosys.2022.109664

Cited by 12 publications

(11 citation statements)

References 48 publications

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“…We first evaluated the query strategies to assess their performance in the absence of outliers. Consistently with the findings reported in our previous work, 16 our results in Figure 2 indicate that the standard CDO algorithm performs best when there are no outliers in the data stream. The use of robust estimators does not provide any added value in this scenario.…”

Section: Methodssupporting

confidence: 92%

“…As in the case of A-optimality, D-optimality has been adapted to the online active learning scenario with the proposal of a CDO algorithm. 16 CDO suggests setting a threshold Γ by using…”

Section: Background and Related Workmentioning

confidence: 99%

“…D‐optimality is another fundamental criterion, 29 which takes both the variances and covariances of the model coefficients into account by maximizing the determinant of the moment matrix M . As in the case of A‐optimality, D‐optimality has been adapted to the online active learning scenario with the proposal of a CDO algorithm 16 . CDO suggests setting a threshold Γ by using

\begin{equation}{{\rm{P}}_{\rm{D}}}\left( {{\bf{x}}_{l + 1}^{\rm{T}}{{\left( {{\bf{X}}_l^{\rm{T}}{{\bf{X}}_l}} \right)}^{ - 1}}{{\bf{x}}_{l + 1}} \ge \Gamma } \right) = \alpha \end{equation}

where

{{\bf{X}}_l}

is the model matrix with the l labeled observations currently available and

{{\bf{x}}_{l + 1}}

is the unlabeled data point that is under evaluation.…”

Section: Background and Related Workmentioning

confidence: 99%

“…The second scenario is referred to as membership query synthesis, and it allows the learner to query the labels of synthetically generated instances rather than those sampled from the process distribution. Finally, the third scenario is online, or stream‐based, active learning 3 . In this case, the unlabeled observations are drawn sequentially by the learner, which must immediately decide whether to keep the instance and query its label or discard it.…”

Section: Introductionmentioning

confidence: 99%

“…An interesting approach to online active learning for fuzzy regression models has been proposed by Lughofer 12 . Other researchers tried to adapt the optimality criteria of the experimental design theory to the online active linear regression framework 13–16 . Linear regression models are still very useful in industrial applications as they can be efficiently trained on a small number of observations.…”

Section: Introductionmentioning

confidence: 99%

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Robust online active learning

Cacciarelli

Külahçı

Tyssedal

2023

Quality & Reliability Eng

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In many industrial applications, obtaining labeled observations is not straightforward as it often requires the intervention of human experts or the use of expensive testing equipment. In these circumstances, active learning can be highly beneficial in suggesting the most informative data points to be used when fitting a model. Reducing the number of observations needed for model development alleviates both the computational burden required for training and the operational expenses related to labeling. Online active learning, in particular, is useful in high‐volume production processes where the decision about the acquisition of the label for a data point needs to be taken within an extremely short time frame. However, despite the recent efforts to develop online active learning strategies, the behavior of these methods in the presence of outliers has not been thoroughly examined. In this work, we investigate the performance of online active linear regression in contaminated data streams. Our study shows that the currently available query strategies are prone to sample outliers, whose inclusion in the training set eventually degrades the predictive performance of the models. To address this issue, we propose a solution that bounds the search area of a conditional D‐optimal algorithm and uses a robust estimator. Our approach strikes a balance between exploring unseen regions of the input space and protecting against outliers. Through numerical simulations, we show that the proposed method is effective in improving the performance of online active learning in the presence of outliers, thus expanding the potential applications of this powerful tool.

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Section: Methodssupporting

confidence: 92%

“…As in the case of A-optimality, D-optimality has been adapted to the online active learning scenario with the proposal of a CDO algorithm. 16 CDO suggests setting a threshold Γ by using…”

Section: Background and Related Workmentioning

confidence: 99%

\begin{equation}{{\rm{P}}_{\rm{D}}}\left( {{\bf{x}}_{l + 1}^{\rm{T}}{{\left( {{\bf{X}}_l^{\rm{T}}{{\bf{X}}_l}} \right)}^{ - 1}}{{\bf{x}}_{l + 1}} \ge \Gamma } \right) = \alpha \end{equation}

where

{{\bf{X}}_l}

is the model matrix with the l labeled observations currently available and

{{\bf{x}}_{l + 1}}

is the unlabeled data point that is under evaluation.…”

Section: Background and Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Robust online active learning

Cacciarelli

Külahçı

Tyssedal

2023

Quality & Reliability Eng

Self Cite

View full text Add to dashboard Cite

show abstract

Active learning for data streams: a survey

Cacciarelli,

Kulahci

2023

Mach Learn

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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 real time. 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.

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Hidden dimensions of the data: PCA vs autoencoders

Cacciarelli,

Kulahci

2023

Quality Engineering

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show abstract

Stream-based active learning with linear models

Cited by 12 publications

References 48 publications

Robust online active learning

Robust online active learning

Active learning for data streams: a survey

Hidden dimensions of the data: PCA vs autoencoders

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