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《Chemphyschem》2006,7(5):1149-1150
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In this paper various ensemble learning methods from machine learning and statistics are considered and applied to the customer choice modeling problem. The application of ensemble learning usually improves the prediction quality of flexible models like decision trees and thus leads to improved predictions. We give experimental results for two real-life marketing datasets using decision trees, ensemble versions of decision trees and the logistic regression model, which is a standard approach for this problem. The ensemble models are found to improve upon individual decision trees and outperform logistic regression. 相似文献
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This paper seeks to solve the difficult nonlinear problem in financial markets on the complex system theory and the nonlinear
dynamics principle, with the data-model-concept-practice issue-oriented reconstruction of the phase space by the high frequency
trade data. In theory, we have achieved the differentiable manifold geometry configuration, discovered the Yang-Mills functional
in financial markets, obtained a meaningful conserved quantity through corresponding space-time non-Abel localization gauge
symmetry transformation, and derived the financial solitons, which shows that there is a strict symmetry between manifold
fiber bundle and guage field in financial markets. In practical applications of financial markets, we have repeatedly carried
out experimental tests in a fluctuant evolvement, directly simulating and validating the existence of solitons by researching
the price fluctuations (society phenomena) using the same methods and criterion as in natural science and in actual trade
to test the stock Guangzhou Proprietary and the futures Fuel Oil in China. The results demonstrate that the financial solitons
discovered indicates that there is a kind of new substance and form of energy existing in financial trade markets, which likely
indicates a new science paradigm in the economy and society domains beyond physics.
相似文献
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Renato Bruni 《Annals of Operations Research》2007,150(1):79-92
The paper is concerned with the problem of binary classification of data records, given an already classified training set
of records. Among the various approaches to the problem, the methodology of the logical analysis of data (LAD) is considered.
Such approach is based on discrete mathematics, with special emphasis on Boolean functions. With respect to the standard LAD
procedure, enhancements based on probability considerations are presented. In particular, the problem of the selection of
the optimal support set is formulated as a weighted set covering problem. Testable statistical hypothesis are used. Accuracy
of the modified LAD procedure is compared to that of the standard LAD procedure on datasets of the UCI repository. Encouraging
results are obtained and discussed. 相似文献
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Mathematical Diagnostics (MD) deals with identification problems arising in different practical areas. Some of these problems
can be described by mathematical models where it is required to identify points belonging to two or more sets of points. Most
of the existing tools provide some identification rule (a classifier) by means of which a given point is assigned (attributed)
to one of the given sets. Each classifier can be viewed as a virtual expert. If there exist several classifiers (experts),
the problem of evaluation of experts’ conclusions arises. In the paper for the case of supervised classification the method
of virtual experts (the VE-method) is described. Based on this method, a generalized VE method is proposed where each of the
classifiers can be chosen from a given family of classifiers. As a result, a new optimization problem with a discontinuous
functional is stated. Examples illustrating the proposed approach are provided.
The work of the second author was supported by the Russian Foundation for Fundamental Studies (RFFI) under Grant No 03-01-00668. 相似文献
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