Dynamic Integration of Regression Models

Rooney, Niall; Patterson, David; Anand, Sarab S.; Tsymbal, Alexey

doi:10.1007/978-3-540-25966-4_16

Cited by 46 publications

(54 citation statements)

References 14 publications

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“…The weights can be constant or dynamically calculated according to each data sample. Popular algorithms to obtain ensemble weights are stacked regression [70] and dynamic weighting [71]. Given a learning set L with K data samples, the stacked regression approach calculates the weights by minimising…”

Section: The Ensemble Learning Strategymentioning

confidence: 99%

Online Multimodal Ensemble Learning Using Self-Learned Sensorimotor Representations

Zambelli

Demiris

2017

IEEE Trans. Cogn. Dev. Syst.

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Internal models play a key role in cognitive agents by providing on the one hand predictions of sensory consequences of motor commands (forward models), and on the other hand inverse mappings (inverse models) to realise tasks involving control loops, such as imitation tasks. The ability to predict and generate new actions in continuously evolving environments intrinsically requiring the use of different sensory modalities is particularly relevant for autonomous robots, which must also be able to adapt their models online. We present a learning architecture based on self-learned multimodal sensorimotor representations. To attain accurate forward models, we propose an online heterogeneous ensemble learning method that allows us to improve the prediction accuracy by leveraging differences of multiple diverse predictors. We further propose a method to learn inverse models on-the-fly to equip a robot with multimodal learning skills to perform imitation tasks using multiple sensory modalities. We have evaluated the proposed methods on an iCub humanoid robot. Since no assumptions are made on the robot kinematic/dynamic structure, the method can be applied to different robotic platforms.

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Section: The Ensemble Learning Strategymentioning

confidence: 99%

Online Multimodal Ensemble Learning Using Self-Learned Sensorimotor Representations

Zambelli

Demiris

2017

IEEE Trans. Cogn. Dev. Syst.

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“…In [25] dynamic integration was considered in the context of ensembles with base classifiers generated on different feature subsets (using so-called ensemble feature selection). In [18] an adaptation of the three dynamic integration techniques to regression is considered and applied for ensembles generated using the random subspace method.…”

Section: Ensemble Learning and Dynamic Integration Of Classifiersmentioning

confidence: 99%

Dynamic integration of classifiers for handling concept drift

et al. 2008

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Abstract. In the real world concepts are often not stable but change with time. A typical example of this in the biomedical context is antibiotic resistance, where pathogen sensitivity may change over time as new pathogen strains develop resistance to antibiotics that were previously effective. This problem, known as concept drift, complicates the task of learning a model from data and requires special approaches, different from commonly used techniques that treat arriving instances as equally important contributors to the final concept. The underlying data distribution may change as well, making previously built models useless. This is known as virtual concept drift. Both types of concept drifts make regular updates of the model necessary. Among the most popular and effective approaches to handle concept drift is ensemble learning, where a set of models built over different time periods is maintained and the best model is selected or the predictions of models are combined, usually according to their expertise level regarding the current concept. In this paper we propose the use of an ensemble integration technique that would help to better handle concept drift at an instance level. In dynamic integration of classifiers, each base classifier is given a weight proportional to its local accuracy with regard to the instance tested, and the best base classifier is selected, or the classifiers are integrated using weighted voting. Our experiments with synthetic data sets simulating abrupt and gradual concept drifts and with a real-world antibiotic resistance data set demonstrate that dynamic integration of classifiers built over small time intervals or fixed-sized data blocks can be significantly better than majority voting and weighted voting, which are currently the most commonly used integration techniques for handling concept drift with ensembles.

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“…We anticipate that local reliability estimation of mass flow prediction [7] may be helpful for the domain experts. We also plan to adopt and develop further the dynamic integration of regression models [8] which may help to improve the reliability of predictions.…”

Section: Conclusion and Further Workmentioning

confidence: 99%

Online Mass Flow Prediction in CFB Boilers

Ivannikov

Pechenizkiy

Bakker

et al. 2009

Advances in Data Mining. Applications and Theoretical Aspects

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Abstract. Fuel feeding and inhomogeneity of fuel typically cause process fluctuations in the circulating fluidized bed (CFB) process. If control systems fail to compensate for the fluctuations, the whole plant will suffer from fluctuations that are reinforced by the closed-loop controls. This phenomenon causes a reduction of efficiency and lifetime of process components. Therefore, domain experts are interested in developing tools and techniques for getting better understanding of underlying processes and their mutual dependencies in CFB boilers. In this paper we consider an application of data mining technology to the analysis of time series data from a pilot CFB reactor. Namely, we present a rather simple and intuitive approach for online mass flow prediction in CFB boilers. This approach is based on learning and switching regression models. Additionally, noise canceling, and windowing mechanisms are used for improving the robustness of online prediction. We validate our approach with a set of simulation experiments with real data collected from the pilot CFB boiler.

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Dynamic Integration of Regression Models

Cited by 46 publications

References 14 publications

Online Multimodal Ensemble Learning Using Self-Learned Sensorimotor Representations

Online Multimodal Ensemble Learning Using Self-Learned Sensorimotor Representations

Dynamic integration of classifiers for handling concept drift

Online Mass Flow Prediction in CFB Boilers

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