The Use of Background Knowledge in Decision Tree Induction
Machine Learning
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Distributional clustering of words for text classification
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Hierarchical classification of Web content
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Probabilistic hierarchical clustering for biological data
Proceedings of the sixth annual international conference on Computational biology
Improving Text Classification by Shrinkage in a Hierarchy of Classes
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Learning the Dimensionality of Hidden Variables
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
The genomics of a signaling pathway: a KDD Cup challenge task
ACM SIGKDD Explorations Newsletter
One class SVM for yeast regulation prediction
ACM SIGKDD Explorations Newsletter
Feature engineering for a gene regulation prediction task
ACM SIGKDD Explorations Newsletter
Comparison of approaches for estimating reliability of individual regression predictions
Data & Knowledge Engineering
Knowledge reduction for decision tables with attribute value taxonomies
Knowledge-Based Systems
Multi-level rough set reduction for decision rule mining
Applied Intelligence
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We propose a Bayesian learning framework which can exploit hierarchical structures of discrete feature domain values to improve the prediction performance on sparse training data. One characteristic of our framework is that it provides a principled way based on mean-variance analysis to transform an original feature domain value to a coarser granularity by exploiting the underlying hierarchical structure. Through this transformation, a tradeoff between precision and robustness is achieved to improve the parameter estimation for prediction. We have conducted comparative experiments using three real-world data sets. The results demonstrate that utilizing domain value hierarchies gains benefits for prediction.