A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
Multivariate discretization of continuous variables for set mining
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering
Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering
Machine Learning
Multivariate discretization for set mining
Knowledge and Information Systems
Machine Learning
An introduction to variable and feature selection
The Journal of Machine Learning Research
Benefitting from the variables that variable selection discards
The Journal of Machine Learning Research
Efficient Feature Selection via Analysis of Relevance and Redundancy
The Journal of Machine Learning Research
Neural Computation
Uniqueness of medical data mining
Artificial Intelligence in Medicine
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This paper presents an intelligent diagnostic supporting system --- i + DiaKAW (Intelligent and Interactive Diagnostic Knowledge Acquisition Workbench), which automatically extracts useful knowledge from massive medical data to support real medical diagnosis. In which, our two novel pre-processing algorithms MIDCA (Multivariate Interdependent Discretization for Continuous-valued Attributes) and LUIFS (Latent Utility of Irrelevant Feature Selection) for continuous feature discretization (CFD) and feature selection (FS) respectively, assist in accelerating the diagnostic accuracy by taking the attributes' supportive relevance into consideration during the data preparation process. Such strategy minimizes the information lost and maximizes the intelligence and accuracy of the system. The empirical results on several real-life datasets from UCI repository demonstrate the goodness of our diagnostic system.