2,639 research outputs found
Computationally efficient versions of conformal predictive distributions
Conformal predictive systems are a recent modification of conformal
predictors that output, in regression problems, probability distributions for
labels of test observations rather than set predictions. The extra information
provided by conformal predictive systems may be useful, e.g., in decision
making problems. Conformal predictive systems inherit the relative
computational inefficiency of conformal predictors. In this paper we discuss
two computationally efficient versions of conformal predictive systems, which
we call split conformal predictive systems and cross-conformal predictive
systems. The main advantage of split conformal predictive systems is their
guaranteed validity, whereas for cross-conformal predictive systems validity
only holds empirically and in the absence of excessive randomization. The main
advantage of cross-conformal predictive systems is their greater predictive
efficiency.Comment: 31 pages, 14 figures, 1 table. The conference version published in
the Proceedings of COPA 2018, and the journal version is to appear in
Neurocomputin
Conformal Prediction: a Unified Review of Theory and New Challenges
In this work we provide a review of basic ideas and novel developments about
Conformal Prediction -- an innovative distribution-free, non-parametric
forecasting method, based on minimal assumptions -- that is able to yield in a
very straightforward way predictions sets that are valid in a statistical sense
also in in the finite sample case. The in-depth discussion provided in the
paper covers the theoretical underpinnings of Conformal Prediction, and then
proceeds to list the more advanced developments and adaptations of the original
idea.Comment: arXiv admin note: text overlap with arXiv:0706.3188,
arXiv:1604.04173, arXiv:1709.06233, arXiv:1203.5422 by other author
ELM regime classification by conformal prediction on an information manifold
Characterization and control of plasma instabilities known as edge-localized modes (ELMs) is crucial for the operation of fusion reactors. Recently, machine learning methods have demonstrated good potential in making useful inferences from stochastic fusion data sets. However, traditional classification methods do not offer an inherent estimate of the goodness of their prediction. In this paper, a distance-based conformal predictor classifier integrated with a geometric-probabilistic framework is presented. The first benefit of the approach lies in its comprehensive treatment of highly stochastic fusion data sets, by modeling the measurements with probability distributions in a metric space. This enables calculation of a natural distance measure between probability distributions: the Rao geodesic distance. Second, the predictions are accompanied by estimates of their accuracy and reliability. The method is applied to the classification of regimes characterized by different types of ELMs based on the measurements of global parameters and their error bars. This yields promising success rates and outperforms state-of-the-art automatic techniques for recognizing ELM signatures. The estimates of goodness of the predictions increase the confidence of classification by ELM experts, while allowing more reliable decisions regarding plasma control and at the same time increasing the robustness of the control system
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