Learning Link-Based Naive Bayes Classifiers from Ontology-Extended Distributed Data

被引：0

作者：

Caragea, Cornelia ^{[1
]}

Caragea, Doina ^{[2
]}

Honavar, Vasant ^{[1
]}

机构：

[1] Iowa State Univ, Dept Comp Sci, Ames, IA 50011 USA

[2] Kansas State Univ, Comp & Informat Sci, Manhattan, KS 66506 USA

来源：

ON THE MOVE TO MEANINGFUL INTERNET SYSTEMS: OTM 2009, PT 2 | 2009年 / 5871卷

关键词：

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

We address the problem of learning predictive models from multiple large, distributed, autonomous, and hence almost invariably semantically disparate, relational data sources from a user's point of view. We show under fairly general assumptions, how to exploit; data sources annotated with relevant meta data in building predictive models (e.g., classifiers) from a collection of distributed relational data sources, without the need for a centralized data warehouse, while offering strong guarantees of exactness of the learned classifiers relative to their centralized relational learning counterparts. We demonstrate an application of the proposed approach in the case of learning link-based Naive Bayes classifiers and present results of experiments on a text classification task that demonstrate the feasibility of the proposed approach.

引用

页码：1139 / +

页数：2