C4.5: programs for machine learning
C4.5: programs for machine learning
Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Feature Selection with Selective Sampling
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
CrossMine: Efficient Classification Across Multiple Database Relations
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
An efficient multi-relational Naïve Bayesian classifier based on semantic relationship graph
MRDM '05 Proceedings of the 4th international workshop on Multi-relational mining
Informative variables selection for multi-relational supervised learning
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
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Feature selection is an essential data processing step to remove the irrelevant and redundant attributes for shorter learning time, better accuracy and better comprehensibility. A number of algorithms have been proposed in both data mining and machine learning area. These algorithms are usually used in single table environment, where data are stored in one relational table or one flat file. They are not suitable for multi-relational environment, where data are stored in multiple tables joined each other by semantic relationships. To solve this problem, in this paper we propose a novel approach called FARSto do both feature and relation selection for efficient multi-relational classification. By this approach, we not only extend traditional feature selection method to selects relevant features from multi-relations, but also develop a new method to reconstruct the multi-relational database schema and get rid of irrelevant tables to further improve classification performance. Results of experiments conducted on several real databases show that FARScan effectively choose a small set of relevant features, enhancing the classification efficiency significantly and improving prediction accuracy.