Building Predictors from Vertically Distributed Data

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Building Predictors from Vertically Distributed Data (2004)

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by Sabine Mcconnell , David B. Skillicorn

Venue:	Proceedings of the 14th Annual IBM Centers for Advanced Studies Conference
Citations:	9 - 0 self

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@INPROCEEDINGS{Mcconnell04buildingpredictors,
    author = {Sabine Mcconnell and David B. Skillicorn},
    title = {Building Predictors from Vertically Distributed Data},
    booktitle = {Proceedings of the 14th Annual IBM Centers for Advanced Studies Conference},
    year = {2004},
    pages = {150--162}
}

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Abstract

Due in part to the large volume of data available today, but more importantly to privacy concerns, data are often distributed across institutional, geographical and organizational boundaries rather than being stored in a centralized location. Data can be distributed by separating objects or attributes: in the homogeneous case, sites contain subsets of objects with all attributes, while in the heterogeneous case sites contain subsets of attributes for all objects. Ensemble approaches combine the results obtained from a number of classifiers to obtain a final classification. In this paper, we present a novel ensemble approach, in which data is partitioned by attributes. We show that this method can successfully be applied to a wide range of data and can even produce an increase in classification accuracy compared to a centralized technique. As an ensemble approach, our technique exchanges models or classification results instead of raw data, which makes it suitable for privacy preserving data mining. In addition, both final model size and runtime are typically reduced compared to a centralized model. The proposed technique is evaluated using a decision tree, a variety of datasets, and several voting schemes. This approach is suitable for physically distributed data as well as privacy preserving data mining. Copyright c ○ 2004 Sabine McConnell and David

Keyphrases

vertically distributed data data mining ensemble approach centralized model raw data wide range final classification homogeneous case several voting scheme centralized location sabine mcconnell final model size organizational boundary novel ensemble approach technique exchange model decision tree privacy concern large volume classification accuracy classification result heterogeneous case data available today

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