Progressive Modeling

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Progressive Modeling (2002)

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by Wei Fan , Haixun Wang , Philip S. Yu , Shaw-hwa Lo , Salvatore Stolfo

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26 - 9 self

BibTeX

@MISC{Fan02progressivemodeling,
    author = {Wei Fan and Haixun Wang and Philip S. Yu and Shaw-hwa Lo and Salvatore Stolfo},
    title = {Progressive Modeling},
    year = {2002}
}

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Abstract

Presently, inductive learning is still performed in a frustrating batch process. The user has little interaction with the system and no control over the final accuracy and training time. If the accuracy of the produced model is too low, all the computing resources are misspent. In this paper, we propose a progressive modeling framework. In progressive modeling, the learning algorithm estimates online both the accuracy of the final model and remaining training time. If the estimated accuracy is far below expectation, the user can terminate training prior to completion without wasting further resources. If the user chooses to complete the learning process, progressive modeling will compute a model with expected accuracy in expected time. We describe one implementation of progressive modeling using ensemble of classifiers.

Keyphrases

progressive modeling training time computing resource frustrating batch process inductive learning final accuracy produced model final model little interaction learning process expected time progressive modeling framework algorithm estimate

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