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Abstract: The overproduce-and-choose strategy, which is divided into the overproduction and selection phases, has traditionally focused on finding the most accurate subset of classifiers at the selection phase, and using it to predict the class of all the samples in the test data set. It is therefore, a static classifier ensemble selection strategy. In this paper, we propose a dynamic overproduce-and-choose strategy which combines optimization and dynamic selection in a two-level selection phase to allow the selection of the most confident subset of classifiers to label each test sample individually. The optimization level is intended to generate a population of highly accurate candidate classifier ensembles, while the dynamic selection level applies measures of confidence to reveal the candidate ensemble with the highest degree of confidence in the current decision. Experimental results conducted to compare the proposed method to a static overproduce-and-choose strategy and a classical dynamic classifier selection approach demonstrate that our method outperforms both these selection-based methods, and is also more efficient in terms of performance than combining the decisions of all classifiers in the initial pool. [Copyright &y& Elsevier]

The article focuses on the continued decline in forest products from the U.S. It was noted that the trade deficit in lumber has grown from 4.1 billion board feet (bbf) in 1962 to 23.4 bbf in 2005, and it is projected to rise to 26.7. bbf by 2050. From 1995 to 2001, the U.S. share of domestic markets fell by 29% for several reasons. With regard to paper and paperboard, the surge in the value of the U.S. dollar from 1996 to 2002 undercut American production, while production expanded in Asia and Latin America, where wage rates are lower and wood fiber plantations are faster growing.