C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Machine Learning
An extensive empirical study of feature selection metrics for text classification
The Journal of Machine Learning Research
An Empirical Study of Feature Selection for Text Categorization based on Term Weightage
WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
A genetic feature weighting scheme for pattern recognition
Integrated Computer-Aided Engineering
Detecting data records in semi-structured web sites based on text token clustering
Integrated Computer-Aided Engineering
Integrated Computer-Aided Engineering
A supervised learning approach to biological question answering
Integrated Computer-Aided Engineering - Selected papers from the IEEE Conference on Information Reuse and Integration (IRI), July 13-15, 2008
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
Integrated Computer-Aided Engineering
Diagnosing multiple faults in oil rig motor pumps using support vector machine classifier ensembles
Integrated Computer-Aided Engineering
A parallel genetic/neural network learning algorithm for MIMD shared memory machines
IEEE Transactions on Neural Networks
Sharing hardware resources in heterogeneous computer-supported collaboration scenarios
Integrated Computer-Aided Engineering
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Radiation shielding has been an active subject of research in the space industry for many years. We propose a new model for mining material properties for radiation shielding. This work represents an effective way of using learning and feature selection for selecting the material properties that most affect the shielding effectiveness of materials. The methodology relies on machine learning as a measure for the identified subsets of material properties for radiation shielding. This is a new direction in working with radiation shielding using purely computational techniques with machine learning. The experimental results showed that the approach is quite effective in eliminating redundant features and identifying the most significant properties related to radiation shielding capability of materials. For example, we have identified some material properties, besides Density, like Heat of Fusion, Atomic Number, X-ray Absorption Edge, Electrical Resistivity, and Specific Heat Capacity that are highly related to the radiation shielding as they have been proved computationally. The evaluation results also show that all machine learning algorithms can induce more robust separating models for the subsets of reduced number of features that are highly significant in the domain of radiation shielding.