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Simple decision forests for multi-relational classification
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DOI：10.1016/j.dss.2012.11.017.png)
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An important task in multi-relational data mining is link-based classification which takes advantage of attributes of links and linked entities, to predict the class label. The relational Naive Bayes classifier exploits independence assumptions to achieve scalability. We introduce a weaker independence assumption to the effect that information from different data tables is independent given the class label. The independence assumption entails a closed-form formula for combining probabilistic predictions based on decision trees learned on different database tables. Logistic regression learns different weights for information from different tables and prunes irrelevant tables. In experiments, learning was very fast with competitive accuracy. (C) 2012 Elsevier B.V. All rights reserved.
Keywords:
Link-based classification
Multi-relational Naive Bayes classifiers
Multi-relational decision trees
Logistic regression
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