C4.5: programs for machine learning
C4.5: programs for machine learning
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Machine Learning
Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
Inductive logic programming for knowedge discovery in databases
Relational Data Mining
Feature Selection Using Rough Sets Theory
ECML '93 Proceedings of the European Conference on Machine Learning
Relative Unsupervised Discretization for Association Rule Mining
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Propositionalisation and Aggregates
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Discovery of Multiple-Level Association Rules from Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Reasoning about Binary Topological Relations
SSD '91 Proceedings of the Second International Symposium on Advances in Spatial Databases
Stochastic Propositionalization of Non-determinate Background Knowledge
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
Inducing Multi-Level Association Rules from Multiple Relations
Machine Learning
Redundant feature elimination for multi-class problems
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Spatial associative classification at different levels of granularity: a probabilistic approach
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
| Hi-index | 0.00 |
In traditional classification setting, training data are represented as a single table, where each row corresponds to an example and each column to a predictor variable or the target variable. However, this propositional (feature-based) representationis quite restrictive when data are organized into several tables of a database. In principle, relational data can be transformed into propositional one by constructing propositional features and performing classification according to some robust and well-known propositional classification methods. Since propositional features should capture relational properties of examples, multi-relational association rules can be adopted in feature construction. Propositionalisation based on relational association rules discovery is implemented in a relational classification framework, named MSRC, tightly integrated with a relational database. It performs the classification at different granularity levels and takes advantage from domain specific knowledge in form of hierarchies and rules. In addition, a feature reduction algorithm is integrated to remove redundant features. An application in classification of real-world geo-referenced census data analysis is reported.