Propositionalization approaches to relational data mining
Relational Data Mining
Simple Estimators for Relational Bayesian Classifiers
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Learning relational probability trees
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
CrossMine: Efficient Classification Across Multiple Database Relations
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Why collective inference improves relational classification
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Naive Bayesian Classification of Structured Data
Machine Learning
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
An Efficient Relational Decision Tree Classification Algorithm
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 03
Discriminative structure and parameter learning for Markov logic networks
Proceedings of the 25th international conference on Machine learning
A Method for Multi-relational Classification Using Single and Multi-feature Aggregation Functions
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
nFOIL: integrating Naïve Bayes and FOIL
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Top-down induction of first-order logical decision trees
Artificial Intelligence
Exploring optimization of semantic relationship graph for multi-relational Bayesian classification
Decision Support Systems
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
A comparison of pruning criteria for probability trees
Machine Learning
Exploring the power of heuristics and links in multi-relational data mining
ISMIS'08 Proceedings of the 17th international conference on Foundations of intelligent systems
Markov Logic: An Interface Layer for Artificial Intelligence
Markov Logic: An Interface Layer for Artificial Intelligence
A Practical Heterogeneous Classifier for Relational Databases
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Discriminative probabilistic models for relational data
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Learning Markov Logic Networks via Functional Gradient Boosting
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
Learning graphical models for relational data via lattice search
Machine Learning
Learning compact Markov logic networks with decision trees
Machine Learning
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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.