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Abstract: Distributed data mining systems aim to discover (and combine) usefull information that is distributed across multiple databases. The JAM system, for example, applies machine learning algorithms to compute models over distributed data sets and employs meta-learning techniques to combine the multiple models. Occasionally, however, these models (or classifiers) are induced from databases that have (moderately) different schemas and hence are incompatible. In this paper, we systematically... (Update)
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...learning task and the characteristics of the different or missing attribute A n 1 of DBB . The details of these approaches can be found in [52]. A n 1 is missing, but can be predicted: It may be possible to create an auxiliary classifier, which we call a bridging agent, from...
...5.2 Bridging methods We describe two methods for handling the missing attributes. The details of these approaches can be found in [7]. Method I: Learn a local model for the missing attribute and exchange. Database DBB imports, along with the remote classifier agent, a...
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BibTeX entry: (Update)
Prodromidis, A., & Stolfo, S. (1998). Mining databases with different schemas: Integrating imcompatible classifiers. In Proc. 4th Intl Conf. Knowledge Discovery and Data Mining, pp. 314--318. http://citeseer.ist.psu.edu/article/prodromidis98mining.html More
@inproceedings{ prodromidis98mining,
author = "Andreas L. Prodromidis and Salvatore J. Stolfo",
title = "Mining Databases with Different Schemas: Integrating Incompatible Classifiers",
booktitle = "Knowledge Discovery and Data Mining",
pages = "314-318",
year = "1998",
url = "citeseer.ist.psu.edu/article/prodromidis98mining.html" }
Citations (may not include all citations):1262 Classification and Regression Trees (context) - Breiman, Friedman et al. - 1984
273 Multivariate adaptive regression splines (context) - Friedman - 1991
248 Fast effective rule induction - Cohen - 1995 DBLP
137 Machine learning research: Four current directions - Dietterich - 1997
113 Locally weighted learning - Moore, Atkeson et al. - 1997 ACM DBLP
86 JAM: Java agents for meta-learning over distributed database.. - Stolfo, Prodromidis et al. - 1997 DBLP
56 Classical and Modern Regression with Applications (context) - Myers - 1986
54 Meta-learning for multistrategy and parallel learning (context) - Chan, Stolfo - 1993
33 Incremental reduced error pruning (context) - Furnkranz, Widmer - 1994
26 Sharing learned models among remote database partitions by l.. - Chan, Stolfo - 1996 DBLP
9 the management of distributed learning agents - Prodromidis - 1997
7 Pruning classifiers in a distributed meta-learning system - Chan, Prodromidis et al. - 1998
4 Boosting and naive bayesian learning [http://wwwcse (context) - Elkan - 1997
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