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  1. Proceedings of the 4th international workshop on Multi-relational mining (MRDM '05)
  2. Gene classification: issues and challenges for relational learning
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Kernel methods for graphs
Relational clustering for multi-type entity resolution
Learning Bayesian networks of rules with SAYU
Mining relational databases with multi-view learning
Qualitative comparison of graph-based and logic-based multi-relational data mining: a case study
Bias-free hypothesis evaluation in multirelational domains
An efficient multi-relational Naïve Bayesian classifier based on semantic relationship graph
Leveraging relational autocorrelation with latent group models
Hyperpaths: extending pathfinding to moded languages
Further results of probabilistic first-order revision of theories from examples
Gene classification: issues and challenges for relational learning
The case for anomalous link detection

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Gene classification: issues and challenges for relational learning

Content Provider ACM Digital Library
Author Merugu, Srujana Perlich, Claudia
Abstract We present ongoing research that applies statistical relational learning techniques, in particular, propositionalization, to the challenging and interesting real-world domain of functional gene classification of the Yeast genome Sachharomyces Cerevisiae. The main objective of this paper is to identify and describe the structural and statistical properties of this domain and examine how they conflict with the assumptions of relational learning approaches. Such properties are, in fact, shared by many relational application domains and potential solutions will be of interest far beyond the particular genetic application. We show in the last part some preliminary experimental results on potential approaches to overcome such limitations by extending the existing automated feature construction strategies to accommodate the specific domain properties.
Starting Page 61
Ending Page 67
Page Count 7
File Format PDF
ISBN 1595932127
DOI 10.1145/1090193.1090204
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2005-08-21
Publisher Place New York
Access Restriction Subscribed
Content Type Text
Resource Type Article
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