DMCA
Simple Decision Forests for Multi-Relational Classification
| Citations: | 2 - 2 self |
BibTeX
@MISC{Bina_simpledecision,
author = {Bahareh Bina and Oliver Schulte and En Crawford and Zhensong Qian and Yi Xiong},
title = {Simple Decision Forests for Multi-Relational Classification},
year = {}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
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 e↵ect that information from di↵erent 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 di↵erent database tables. Logistic regression learns di↵erent weights for information from di↵erent tables and prunes irrelevant tables. In experiments, learning was very fast with competitive accuracy.
Keyphrases
multi-relational classification simple decision forest independence assumption class label multi-relational data mining di erent data table logistic regression di erent weight decision tree closed-form formula link-based classification probabilistic prediction important task di erent table prune irrelevant table di erent database table competitive accuracy
