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12th Conference on Artificial Intelligence in Medicine (AIME 2009)

作者： Zhdanov, Fedor Vovk, Vladimir Burford, Brian Devetyarov, Dmitry Nouretdinov, Ilia Gammerman, Alex Univ London Dept Comp Sci Royal Holloway Comp Learning Res Ctr Egham TW20 0EX Surrey England

ISBN: (纸本)9783642029752

In this paper we apply computer learning methods to the diagnosis of ovarian cancer using the level of the standard biomarker CA125 in conjunction with information provided by mass spectrometry. Our algorithm gives probability predictions for the disease. To check the power of our algorithm we use it to test, the hypothesis that CA125 and the peaks do not, contain useful information for the prediction of the disease at a particular time before the diagnosis. It produces p-values that are less than those produced by all algorithm that has, been previously applied to this data set. Our conclusion is that the proposed algorithm is especially reliable for prediction the ovarian cancer on some stages.

关键词： Online prediction aggregating algorithm ovarian Cancer mass spectrometry proteomics

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Competitive Online Regression under Continuous Ranked Probability Score 8

Competitive Online Regression under Continuous Ranked Probab...

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8th Symposium on Conformal and Probabilistic Predictions with Applications (COPA)

作者： Dzhamtyrova, Raisa Kalnishkan, Yuri Royal Holloway Univ London Dept Comp Sci Egham Surrey England Moscow Inst Phys & Technol Lab Adv Combinator & Network Applicat Egham Surrey England

We consider the framework of competitive prediction when one provides guarantees compared to other predictive models that are called experts. We propose the algorithm that combines point predictions of an infinite pool of linear experts and outputs probability forecasts in the form of cumulative distribution functions. We evaluate the quality of probabilistic prediction by the continuous ranked probability score (CRPS), which is a widely used proper scoring rule. We provide a strategy that allows us to 'track the best expert' and derive the theoretical bound on the discounted loss of the strategy. Experimental results on synthetic data and solar power data show that the theoretical bounds of our algorithm are not violated. Also the algorithm performs close to and sometimes outperforms the retrospectively best quantile regression.

关键词： prediction with expert advice online learning competitive prediction aggregating algorithm continuous ranked probability score (CRPS) probabilistic forecasting

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Supermartingales in prediction with expert advice

Supermartingales in prediction with expert advice

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19th International Conference on algorithmic Learning Theory

作者： Chernov, Alexey Kalnishkan, Yuri Zhdanov, Fedor Vovk, Vladimir Univ London Comp Learning Res Ctr Dept Comp Sci Egham TW20 0EX Surrey England

ISBN: (纸本)9783540879862

The paper applies the method of defensive forecasting, based on the use of game-theoretic supermartingales, to prediction with expert advice. In the traditional setting of a countable number of experts and a finite number of outcomes, the Defensive Forecasting algorithm is very close to the well-known aggregating algorithm. Not only the performance guarantees but also the predictions are the same for these two methods of fundamentally different nature. The paper also discusses a new setting where the experts can give advice conditional on the learner's future decision. Both the algorithms can be adapted to the new setting and give the same performance guarantees as in the traditional setting. Finally, an application of defensive forecasting to a setting with several loss functions is outlined. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Prediction with expert advice Defensive forecasting algorithm aggregating algorithm

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Mixing Past Predictions 9

Mixing Past Predictions

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9th Symposium on Conformal and Probabilistic Predictions with Applications (COPA)

作者： Korotin, Alexander V'yugin, Vladimir Burnaev, Evgeny Skolkovo Inst Sci & Technol Moscow Russia Inst Informat Transmiss Problems Moscow Russia

In the framework of the theory of prediction with expert advice, we present an algorithm for online aggregation of the functional predictions. The approach implies that at each time step some algorithm issues a forecast in the form of a function and then the master algorithm combines these current and past functional forecasts into one aggregated functional forecast. We apply the proposed algorithm for the problem of long-term predictions of time series. By combining the past and current long-term functional forecasts, we obtain a smoothing mechanism that protects our algorithm from temporary changes in the trend of time series, noise and outliers. To evaluate the performance of presented aggregating algorithm as a long-term forecaster we use a new "integral" loss function and the delayed feedback approach. We apply this algorithm for the regression problems, we present some method for smoothing regression forecasts.

关键词： Functional forecasts Integral loss function Mixable loss function aggregating algorithm Long-term online forecasting Prediction with expert advice Online smoothing regression

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Competitive Online Generalized Linear Regression under Square Loss

Competitive Online Generalized Linear Regression under Squar...

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European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Zhdanov, Fedor Vovk, Vladimir Univ London Comp Learning Res Ctr Egham TW20 0EX Surrey England Univ London Royal Holloway & Bedford New Coll Egham TW20 0EX Surrey England

ISBN: (纸本)9783642159381

We apply the aggregating algorithm to the problem of online regression under the square loss function. We develop an algorithm competitive with the benchmark class of generalized linear models (our "experts"), which are used in a wide range of practical tasks. This problem does not appear to be analytically tractable. Therefore, we develop a prediction algorithm using the Markov chain Monte Carlo method, which is shown to be fast and reliable in many cases. We prove upper bounds on the cumulative square loss of the algorithm. We also perform experiments with our algorithm on a toy data set and two real world ozone level data sets and give suggestions about choosing its parameters.

关键词： online prediction aggregating algorithm square loss function generalized linear models

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Predicting nearly as well as the best pruning of a decision tree through dynamic programming scheme

Predicting nearly as well as the best pruning of a decision ...

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8th International Workshop on algorithmic Learning Theory

作者： Takimoto, E Maruoka, A Vovk, V Tohoku Univ Grad Sch Informat Sci Sendai Miyagi 9808579 Japan Univ London Royal Holloway & Bedford New Coll Dept Comp Sci Egham TW20 0EX Surrey England

Helmbold and Schapire gave an on-line prediction algorithm that, when given an unpruned decision tree, produces predictions not much worse than the predictions made by the best pruning of the given decision tree. In this paper, we give two new on-line algorithms. The first algorithm is based on the observation that finding the best pruning can be efficiently solved by a dynamic programming in the "batch" setting where all the data to be predicted are given in advance. This algorithm works well for a wide class of Loss functions, whereas the one given by Helmbold and Schapire is only described for the absolute loss function. Moreover, the algorithm given in this paper is so simple and general that it could be applied to many other on-line optimization problems solved by dynamic programming. We also explore the second algorithm that is competitive not only with the best pruning but also with the best prediction values which are associated with nodes in the decision tree. In this setting, a greatly simplified algorithm is given for the absolute loss function. It can be easily generalized to the case where, instead of using decision trees, data are classified in some arbitrarily fixed manner. (C) 2001 Elsevier Science B.V. All rights reserved.

关键词： on-line prediction pruning decision tree expert model aggregating algorithm

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Practical Investment with the Long-Short Game 9

Practical Investment with the Long-Short Game

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9th Symposium on Conformal and Probabilistic Predictions with Applications (COPA)

作者： Al-baghdadi, Najim Kalnishkan, Yuri Lindsay, David Lindsay, Sian Royal Holloway Univ London Dept Comp Sci Egham Surrey England Moscow Inst Phys & Technol Lab Adv Combinator & Network Applicat Inst Sky Per 9 Dolgoprudnyi 41701 Russia AlgoLabs Bracknell Berks England

In this paper we apply the aggregating algorithm, an on-line prediction with expert advice algorithm, to real-world foreign exchange trading data with the aim of finding investment strategies with optimal returns. We consider the Long-Short game first introduced in Vovk and Watkins (1998) and it's implementation, including the derivation of expert predictions from model trading data. Furthermore, we propose modifications to improve the practical performance of the game with respect to well-known portfolio performance indicators.

关键词： Prediction with Expert Advice Online Learning aggregating algorithm Portfolio Optimisation Long-Short Game Foreign Exchange Currency Trading

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Aggregation of Adaptive Forecasting algorithms Under Asymmetric Loss Function 3rd

Aggregation of Adaptive Forecasting Algorithms Under Asymmet...

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3rd International Symposium on Statistical Learning and Data Sciences (SLDS)

作者： Romanenko, Alexey Moscow Inst Phys & Technol Dolgoprudnyi 141700 Moscow Region Russia

ISBN: (纸本)9783319170916;9783319170909

The paper deals with applying the strong aggregating algorithm to games with asymmetric loss function. A particular example of such games is the problem of time series forecasting where specific losses from under-forecasting and over-forecasting may vary considerably. We use the aggregating algorithm for building compositions of adaptive forecasting algorithms. The paper specifies sufficient conditions under which a composition based on the aggregating algorithm performs as well as the best of experts. As a result, we find a theoretical bound for the loss process of a given composition under asymmetric loss function. Finally we compare the composition based on the aggregating algorithm to other well-known compositions in experiments with real data.

关键词： aggregating algorithm Time series forecasting Asymmetric loss function

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Online Learning with Continuous Ranked Probability Score

Online Learning with Continuous Ranked Probability Score

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8th Symposium on Conformal and Probabilistic Predictions with Applications (COPA)

作者： V'yugin, Vladimir V. Trunov, Vladimir G. Inst Informat Transmiss Problems Moscow Russia Skolkovo Inst Sci & Technol Moscow Russia

Probabilistic forecasts in the form of probability distributions over future events have become popular in several fields of statistical science. The dissimilarity between a probability forecast and an outcome is measured by a loss function (scoring rule). Popular example of scoring rule for continuous outcomes is the continuous ranked probability score (CRPS). We consider the case where several competing methods produce online predictions in the form of probability distribution functions. In this paper, the problem of combining probabilistic forecasts is considered in the prediction with expert advice framework. We show that CRPS is a mixable loss function and then the time-independent upper bound for the regret of the Vovk's aggregating algorithm using CRPS as a loss function can be obtained. We present the results of numerical experiments illustrating the proposed methods.

关键词： On-line learning Prediction with Expert Advice aggregating algorithm Probabilistic prediction Continuous Ranked Probability Score CRPS Mixability

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Online Aggregation of Conformal Forecasting Systems

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JOURNAL OF COMMUNICATIONS TECHNOLOGY AND ELECTRONICS 2023年第SUPPL 2期68卷 S239-S253页

作者： V'yugin, V. V. Trunov, V. G. Russian Acad Sci Kharkevich Inst Informat Transmiss Problems Moscow 127051 Russia

The problem of online probabilistic time series forecasting is considered. Probabilistic forecasts are obtained as a result of the application of conformal forecasting systems. The conformal forecasting system is defined on the basis of point forecasts of the regression algorithm. The corresponding probability distribution function is used to assess the degree of reliability of the algorithm's predictions. The paper considers the case when at each moment of time several competing methods (experts) present their forecasts in the form of distribution functions. These distributions are constructed online from point predictions using the conformal prediction method. The probabilistic forecasts of experts are combined using an aggregating algorithm into one probabilistic forecast at each stage of the forecasting process, while expert forecasts can be used at a discount. A technology has been developed for constructing predictive expert algorithms and aggregation of their probabilistic forecasts on the example of the problem of forecasting the hourly load of an electric network depending on the temperature forecast. The results of numerical experiments on real data are presented;a comparative analysis of the method of conformal predictions and the method of Gaussian mixtures is carried out.

关键词： machine learning online probabilistic forecasting conformal forecasting systems forecast calibration prediction with expert advice regret confidence levels aggregating algorithm continuous ranked probability score (CRPS) CRPS forecasting in an electric network

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