Original Contribution: Stacked generalization
Neural Networks
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Category learning through multimodality sensing
Neural Computation
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Information Retrieval
Machine Learning
Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
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
Top-Down Induction of Clustering Trees
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Aggregation-based feature invention and relational concept classes
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-relational data mining: an introduction
ACM SIGKDD Explorations Newsletter
Active learning with multiple views
Active learning with multiple views
CrossMine: Efficient Classification Across Multiple Database Relations
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Learning from imbalanced data sets with boosting and data generation: the DataBoost-IM approach
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Mining relational data through correlation-based multiple view validation
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining for combined association rules on multiple datasets
Proceedings of the 2007 international workshop on Domain driven data mining
Multirelational classification: a multiple view approach
Knowledge and Information Systems
Learning from Skewed Class Multi-relational Databases
Fundamenta Informaticae - Progress on Multi-Relational Data Mining
Measuring to fit: virtual tailoring through cluster analysis and classification
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Learning from Skewed Class Multi-relational Databases
Fundamenta Informaticae - Progress on Multi-Relational Data Mining
Combining heterogeneous classifiers for relational databases
Pattern Recognition
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Most of today's structured data resides in relational databases where multiple relations are formed by foreign key joins. In recent years, the field of data mining has played a key role in helping humans analyze and explore large databases. Unfortunately, most methods only utilize "flat" data representations. Thus, to apply these single-table data mining techniques, we are forced to incur a computational penalty by first converting the data into this "flat" form. As a result of this transformation, the data not only loses its compact representation but the semantic information present in the relations are reduced or eliminated. In this paper, we describe a classification approach, which addresses this issue by operating directly on relational databases. The approach, called MVC (Multi-View Classification), is based on a multi-view learning framework. In this framework, the target concept is represented in different views and then independently learned using single-table data mining techniques. After constructing multiple classifiers for the target concept in each view, the learners are validated and combined by a meta-learning algorithm. Two methods are employed in the MVC approach, namely (1) target concept propagation and (2) multi-view learning. The propagation method constructs training sets directly from relational databases for use by the multi-view learners. The learning method employs traditional single-table mining techniques to mine data straight from a multi-relational database. Our experiments on benchmark real-world databases show that the MVC method achieves promising results in terms of overall accuracy obtained and run time, when compared with the FOIL and CrossMine learning methods.