Handling numeric attributes with ant colony based classifier for medical decision making

• We implement an ACO method named nAnt-Miner with focus on numeric attributes.
• nAnt-Miner is comparable with existing method but can also detect strong conditional dependencies between numeric attributes.
• The method achieves better results on real medical dataset than existing method.

In data mining many datasets are described with both discrete and numeric attributes. Most Ant Colony Optimization based classifiers can only deal with discrete attributes and need a pre-processing discretization step in case of numeric attributes. We propose an adaptation of AntMiner+ for rule mining which intrinsically handles numeric attributes. We describe the new approach and compare it to the existing algorithms. The proposed method achieves comparable results with existing methods on UCI datasets, but has advantages on datasets with strong interactions between numeric attributes. We analyse the effect of parameters on the classification accuracy and propose sensible defaults. We describe application of the new method on a real world medical domain which achieves comparable results with the existing method.