On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Combining support vector and mathematical programming methods for classification
Advances in kernel methods
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Inductive logic programming for knowedge discovery in databases
Relational Data Mining
Reliable Classifications with Machine Learning
ECML '02 Proceedings of the 13th European Conference on Machine Learning
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Attribute-Value Learning Versus Inductive Logic Programming: The Missing Links (Extended Abstract)
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
Learning from Labeled and Unlabeled Data using Graph Mincuts
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Learning with progressive transductive support vector machine
Pattern Recognition Letters
A study of distance-based machine learning algorithms
A study of distance-based machine learning algorithms
Naive Bayesian Classification of Structured Data
Machine Learning
Spatial associative classification: propositional vs structural approach
Journal of Intelligent Information Systems
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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Transduction is an inference mechanism "from particular to particular". Its application to classification tasks implies the use of both labeled (training) data and unlabeled (working) data to build a classifier whose main goal is that of classifying (only) unlabeled data as accurately as possible. Unlike the classical inductive setting, no general rule valid for all possible instances is generated. Transductive learning is most suited for those applications where the examples for which a prediction is needed are already known when training the classifier. Several approaches have been proposed in the literature on building transductive classifiers from data stored in a single table of a relational database. Nonetheless, no attention has been paid to the application of the transduction principle in a (multi-)relational setting, where data are stored in multiple tables of a relational database. In this paper we propose a new transductive classifier, named TRANSC, which is based on a probabilistic approach to making transductive inferences from relational data. This new method works in a transductive setting and employs a principled probabilistic classification in multi-relational data mining to face the challenges posed by some spatial data mining problems. Probabilistic inference allows us to compute the class probability and return, in addition to result of transductive classification, the confidence in the classification. The predictive accuracy of TRANSC has been compared to that of its inductive counterpart in an empirical study involving both a benchmark relational dataset and two spatial datasets. The results obtained are generally in favor of TRANSC, although improvements are small by a narrow margin.