Original Contribution: Stacked generalization
Neural Networks
Machine Learning
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Probabilistic frame-based systems
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Inductive Logic Programming: Derivations, Successes and Shortcomings
ECML '93 Proceedings of the European Conference on Machine Learning
ECML '93 Proceedings of the European Conference on Machine Learning
Learning Probabilistic Models of Relational Structure
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Combining Labeled and Unlabeled Data for MultiClass Text Categorization
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
An Efficient Boosting Algorithm for Combining Preferences
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
CrossMine: Efficient Classification Across Multiple Database Relations
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Naive Bayesian Classification of Structured Data
Machine Learning
Mining relational databases with multi-view learning
MRDM '05 Proceedings of the 4th international workshop on Multi-relational mining
An efficient multi-relational Naïve Bayesian classifier based on semantic relationship graph
MRDM '05 Proceedings of the 4th international workshop on Multi-relational mining
Fundamentals of Database Systems (5th Edition)
Fundamentals of Database Systems (5th Edition)
An Efficient Relational Decision Tree Classification Algorithm
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 03
Matrix-pattern-oriented least squares support vector classifier with AdaBoost
Pattern Recognition Letters
Probabilistic classification and clustering in relational data
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Top-down induction of first-order logical decision trees
Artificial Intelligence
Applied Data Mining for Business and Industry
Applied Data Mining for Business and Industry
Discretization numbers for multiple-instances problem in relational database
ADBIS'07 Proceedings of the 11th East European conference on Advances in databases and information systems
A novel multi-view learning developed from single-view patterns
Pattern Recognition
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Practical usage of machine learning is gaining strategic importance in enterprises looking for business intelligence. However, most enterprise data is distributed in multiple relational databases with expert-designed schema. Using traditional single-table machine learning techniques over such data not only incur a computational penalty for converting to a flat form (mega-join), even the human-specified semantic information present in the relations is lost. In this paper, we present a practical, two-phase hierarchical meta-classification algorithm for relational databases with a semantic divide and conquer approach. We propose a recursive, prediction aggregation technique over heterogeneous classifiers applied on individual database tables. The proposed algorithm was evaluated on three diverse datasets, namely TPCH, PKDD and UCI benchmarks and showed considerable reduction in classification time without any loss of prediction accuracy.