A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Classification Rule Discovery with Ant Colony Optimization
IAT '03 Proceedings of the IEEE/WIC International Conference on Intelligent Agent Technology
Benchmarking Attribute Selection Techniques for Discrete Class Data Mining
IEEE Transactions on Knowledge and Data Engineering
Ant Colony Optimization
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Proceedings of the 2007 ACM symposium on Applied computing
Rule Mining Algorithm with a New Ant Colony Optimization Algorithm
ICCIMA '07 Proceedings of the International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007) - Volume 02
An efficient ant colony optimization approach to attribute reduction in rough set theory
Pattern Recognition Letters
Text feature selection using ant colony optimization
Expert Systems with Applications: An International Journal
Classification rule mining with an improved ant colony algorithm
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Data mining with an ant colony optimization algorithm
IEEE Transactions on Evolutionary Computation
Fuzzy-Rough Sets Assisted Attribute Selection
IEEE Transactions on Fuzzy Systems
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Attribute Selection (AS) is generally applied as a data pre-processing step to sufficiently reduce the number of attributes in a dataset. This study uses six different data mining AS methods to identify a few key driving climate and air pollution attributes from small attribute sets (16 attributes) to increase knowledge about the underlying structures of acute respiratory hospital admission counts, because understanding key factors in environmental science data helps constructing a cost effective data collection and management process by focusing on collecting and investigating more representative and important variables. The performance of the selected attribute set was tested with Ant-Miner and C4.5 classifiers to examine the ability to prediction the admission count. Removal of attributes was successful over all AS methods, especially TNSU (a newly developed AS method, Tree Node Selection for unpruned), which achieved best in removing attributes and some improving the classification accuracy for Ant-Miner and C4.5. However, the overall prediction accuracy improvements are small, suggesting that AS selects attribute sets sufficiently enough to maintain the accuracy for Ant-Miner and C4.5.