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Committee, Expert Advice, and the weighted majority algorithm: An Application to the Pricing Decision of a Monopolist

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COMPUTATIONAL ECONOMICS 2010年第3期35卷 245-267页

作者： Braouezec, Yann Ecole Super Ingenieurs Leonard de Vinci Dept Ingn Financiere F-92216 Paris France

We consider a repeated pricing decision problem of a monopolist (the decision-maker) who does not know the demand function of some new product, and hence the profit function. To decide, she is helped by a committee of N experts. Each expert has an estimation of the unknown demand function and use it to advise the decision-maker on how she should modify the current price. Decisions are taken with a weighted majority rule, where the weight of each expert, which may be interpreted as her decision power, evolves as a function of its accuracy. When a perfect exists, i.e., who always gives the correct advice, we show that she ends up with all the decision power in the long-run so that the decision-maker finds the optimal price. When such a perfect does not exist, the decision-maker is actually unable to consistently select an expert over time so that the sequences of prices and weights describe a limit cycle. Interestingly enough, if the decision-maker takes a large sample of the stationary behavior of prices, the empirical mean turns out to be arbitrarily close to the optimal price, independently of the "quality" of the experts, as long as there experts are "diverse" enough. This result gives thus support to the thesis developed in the books of Surowiecki (The wisdom of the crowd. Why the many are smarter than the few and how collective wisdom shapes business, economies, societies and nations, 2005) and Page (The difference: How the power of diversity creates better groups, firms, schools, and societies, 2007).

关键词： Experts Diversity Learning weighted majority algorithm Sample mean Surowiecki

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Enhancing the blockchain voting process in IoT using a novel blockchain weighted majority Consensus algorithm (WMCA)

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INFORMATION SECURITY JOURNAL 2022年第2期31卷 125-143页

作者： Alhejazi, Manal Mohamed Mohammad, Rami Mustafa A. Imam Abdulrahman Bin Faisal Univ Comp Sci Dept Coll Comp Sci & Informat Technol Dammam Saudi Arabia Imam Abdulrahman Bin Faisal Univ Comp Informat Syst Dept Coll Comp Sci & Informat Technol Dammam Saudi Arabia

Internet of Things (IoT) is expected to improve our lifestyle in a noticeable way. However, although the IoT holds a lot of chances, it contains a lot of serious risks. This leads to a focus on developing security techniques that can increase the security level of IoT. Blockchain is considered as an innovative technique for securing IoT and sharing data in a secure and tamperproof way. The blockchain is a peer-to-peer connection system that performs transactions securely by using consensus algorithms with no need for a trusted third party. Blockchain proved its applicability in securing IoT networks, and the research in this area is still enticing researchers to delve deeper and deeper. Decentralized voting is considered the fundamental principle that blockchain relies on for making the appropriate decision that would offer a proper security level for IoT. In this research a novel decentralized blockchain weighted majority Consensus algorithm is proposed. The algorithm is inspired by the well-known weighted majority voting algorithm in the ensemble data mining learning approach. A java implementation of WMCA has been created for testing several scenarios with the aim of confirming the applicability of the proposed WMCA and the results were very promising.

关键词： Internet of things (IoT) blockchain consensus ensemble weighted majority algorithm

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An on-line prediction algorithm combining several prediction strategies in the shared bet model

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 1999年第2期E82D卷 348-355页

作者： Tajika, I Takimoto, E Maruoka, A Tohoku Univ Grad Sch Informat Sci Sendai Miyagi 9808579 Japan

One of the most important problems in machine learning is to predict a binary value by observing a sequence of outcomes, up to the present time step, generated from some unknown source. Vovk and Cesa-Bianchi ct al. independently proposed an on-line prediction model where prediction algorithms are assumed to be given a collection of prediction strategies called experts and hence be allowed to use the predictions they make. In this model, no assumption is made about the way the sequence of bits to be predicted is generated, and the performance of the algorithm is measured by the difference between the number of mistakes it makes on the bit sequence and the number of mistakes made by the best expert, on the same sequence. In this paper we extend the model by introducing a notion of investment. That is, both the prediction algorithm and the experts are required to make bets on their predictions at each time step, and the performance of the algorithm is now measured with respect to the total money lost, rather than the number of mistakes. We analyze our algorithms in the particular situation where all the experts share the same amount of bets at each time step. In this shared bet model, we give a prediction algorithm that is in some sense optimal but impractical, and we also give an efficient prediction algorithm that turns out to be nearly optimal.

关键词： on-line prediction model prediction strategy minimax strategy weighted majority algorithm

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Online variance minimization

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MACHINE LEARNING 2012年第1期87卷 1-32页

作者： Warmuth, Manfred K. Kuzmin, Dima UC Calif Santa Cruz CA USA Google Mountain View CA USA

We consider the following type of online variance minimization problem: In every trial t our algorithms get a covariance matrix C-t and try to select a parameter vector w(t-1) such that the total variance over a sequence of trials Sigma(T)(t=1)(w(t-1))(Tau)C(t)w(t-1) is not much larger than the total variance of the best parameter vector u chosen in hindsight. Two parameter spaces in R-n are considered-the probability simplex and the unit sphere. The first space is associated with the problem of minimizing risk in stock portfolios and the second space leads to an online calculation of the eigenvector with minimum eigenvalue of the total covariance matrix Sigma C-T(t=1)t. For the first parameter space we apply the Exponentiated Gradient algorithm which is motivated with a relative entropy regularization. In the second case, the algorithm has to maintain uncertainty information over all unit directions u. For this purpose, directions are represented as dyads uu(Tau) and the uncertainty over all directions as a mixture of dyads which is a density matrix. The motivating divergence for density matrices is the quantum version of the relative entropy and the resulting algorithm is a special case of the Matrix Exponentiated Gradient algorithm. In each of the two cases we prove bounds on the additional total variance incurred by the online algorithm over the best offline parameter.

关键词： Hedge algorithm weighted majority algorithm Online learning Expert setting Density matrix Matrix exponentiated gradient algorithm Quantum relative entropy

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Transfer learning for class imbalance problems with inadequate data

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KNOWLEDGE AND INFORMATION SYSTEMS 2016年第1期48卷 201-228页

作者： Al-Stouhi, Samir Reddy, Chandan K. Honda Automobile Technol Res Southfield MI USA Wayne State Univ Dept Comp Sci Detroit MI 48202 USA

A fundamental problem in data mining is to effectively build robust classifiers in the presence of skewed data distributions. Class imbalance classifiers are trained specifically for skewed distribution datasets. Existing methods assume an ample supply of training examples as a fundamental prerequisite for constructing an effective classifier. However, when sufficient data are not readily available, the development of a representative classification algorithm becomes even more difficult due to the unequal distribution between classes. We provide a unified framework that will potentially take advantage of auxiliary data using a transfer learning mechanism and simultaneously build a robust classifier to tackle this imbalance issue in the presence of few training samples in a particular target domain of interest. Transfer learning methods use auxiliary data to augment learning when training examples are not sufficient and in this paper we will develop a method that is optimized to simultaneously augment the training data and induce balance into skewed datasets. We propose a novel boosting-based instance transfer classifier with a label-dependent update mechanism that simultaneously compensates for class imbalance and incorporates samples from an auxiliary domain to improve classification. We provide theoretical and empirical validation of our method and apply to healthcare and text classification applications.

关键词： Rare class Transfer learning Class imbalance AdaBoost weighted majority algorithm HealthCare informatics Text mining

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On-line prediction and conversion strategies

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MACHINE LEARNING 1996年第1期25卷 71-110页

作者： CesaBianchi, N Freund, Y Helmbold, DP Warmuth, MK AT&T BELL LABS MURRAY HILL NJ 07974 USA UNIV CALIF SANTA CRUZ DEPT COMP SCI SANTA CRUZ CA 95064 USA

We study the problem of deterministically predicting boolean values by combining the boolean predictions of several experts. Previous on-line algorithms for this problem predict with the weighted majority of the experts' predictions. These algorithms give each expert an exponential weight beta(m) where beta is a constant in [0, 1) and m is the number of mistakes made by the expert in the past. We show that it is better to use sums of binomials as weights. In particular, we present a deterministic algorithm using binomial weights that has a better worst case mistake bound than the best deterministic algorithm using exponential weights. The binomial weights naturally arise from a version space argument. We also show how both exponential and binomial weighting schemes can be used to make prediction algorithms robust against noise.

关键词： on-line learning conversion strategies noise robustness binomial weights exponential weights weighted majority algorithm expert advice mistake bounds Ulam's game

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Simulation and Pricing Mechanism Analysis of a Solar-Powered Electrical Microgrid

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IEEE SYSTEMS JOURNAL 2010年第3期4卷 275-284页

作者： Maity, Indrani Rao, Shrisha Comp Visionlabs Pvt Ltd Hyderabad Andhra Pradesh India Int Inst Informat Technol Bangalore Bangalore Karnataka India

A framework is proposed for an electrical power microgrid, such as for a colony or small township of homes that generate electrical power from solar energy and use it directly when possible, and via stored battery power at other times. The situation is described as a demand and supply problem in a multi-agent system with many consumers and suppliers and no explicit communication or coordination among the agents. Such a demand and supply problem is modeled as a Potluck Problem, a generalization of the Santa Fe Bar Problem. Power produced by PV panels and batteries may be used in the local market, in addition to being consumed locally. The proposed microgrid system model is able to determine the optimum operation of a solar-powered microgrid with respect to load demand, environmental requirements, PV panel and battery capacities. The results indicate the effect of various such parameters on the performance of these micro-grids. This paper also analyzes and proposes, based on auction theory, the most efficient and competing pricing mechanism in the proposed microgrid system model. Two important market bidding techniques, single bidding and discriminatory bidding, are considered. The microgrid is made to participate in the bidding process to serve the consumers at a reduced price and to provide better revenues. The viability of the model proposed is illustrated with analyses using realistic assumptions and published historical data.

关键词： Auction theory electricity markets load management market design microgrid Potluck Problem Santa Fe Bar Problem solar energy weighted majority algorithm

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Air Cargo Demand Modeling and Prediction

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IEEE SYSTEMS JOURNAL 2014年第1期8卷 52-62页

作者： Totamane, Raghavendra Dasgupta, Amit Rao, Shrisha Unisys Global Serv India Bangalore 560025 Karnataka India Int Inst Informat Technol Bangalore Bangalore 560100 Karnataka India

The air cargo transportation system is a large and complex service system, in which demand forecasting is a key element in the master planning process. Demand forecasting is essential for analyzing existing cargo flight schedules and identifying future facility requirements of air cargo companies. We use the Potluck Problem approach to propose a multiproducer/multiconsumer solution for predicting the cargo demand of a specific airline in a given route, and the cargo load factor for a given flight schedule on that route. This solution considers each airline as a producer and the users of air cargo services as consumers, with a producer having no explicit communication with other producers/airlines. The model analyzes the existing cargo capacity plan, highlights drawbacks, and proposes a new capacity plan to demonstrate the effectiveness of using the solution. Examples are provided to illustrate the efficacy of the approach.

关键词： Air cargo cargo capacity cargo load factor demand prediction multiagent system Potluck Problem weighted majority algorithm

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Adaptive Boosting for Transfer Learning Using Dynamic Updates

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

作者： Al-Stouhi, Samir Reddy, Chandan K. Wayne State Univ Dept Comp Engn Detroit MI 48202 USA Wayne State Univ Dept Comp Sci Detroit MI 48202 USA

ISBN: (纸本)9783642237805;9783642237799

Instance-based transfer learning methods utilize labeled examples from one domain to improve learning performance in another domain via knowledge transfer. Boosting-based transfer learning algorithms are a subset of such methods and have been applied successfully within the transfer learning community. In this paper, we address some of the weaknesses of such algorithms and extend the most popular transfer boosting algorithm, TrAdaBoost. We incorporate a dynamic factor into TrAdaBoost to make it meet its intended design of incorporating the advantages of both AdaBoost and the "weighted majority algorithm". We theoretically and empirically analyze the effect of this important factor on the boosting performance of TrAdaBoost and we apply it as a "correction factor" that significantly improves the classification performance. Our experimental results on several real-world datasets demonstrate the effectiveness of our framework in obtaining better classification results.

关键词： Transfer learning AdaBoost TrAdaBoost weighted majority algorithm

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Label Space Transfer Learning

Label Space Transfer Learning

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IEEE 24th International Conference on Tools with Artificial Intelligence (ICTAI)

作者： Al-Stouhi, Samir Reddy, Chandan K. Lanfear, David E. Wayne State Univ Dept Comp Engn Detroit MI 48202 USA

ISBN: (纸本)9780769549156;9781479902279

Small datasets pose a tremendous challenge in machine learning due to the few available training examples compounded with the relative rarity of certain labels which can potentially impede the development of a representative hypothesis. We define "Rare Datasets" as ones with low samples/features ratio and a skewed label distribution. Since a generalized training model can not be theoretically guaranteed, a method to leverage similar data is needed. We propose the first algorithm that utilizes transfer learning for the label space, present theoretical verification of our method and demonstrate the effectiveness of our framework with several real-world experiments. In addition, we formally describe what constitutes a "Rare Dataset" and present a detailed characterization of related methods.

关键词： Rare class transfer learning class imbalance AdaBoost weighted majority algorithm healthcare

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