Abstract
We describe a data mining model for constructing an optimal diagnostic sequence that assists cost-effective sequential decisions. We use heuristic search, i.e., hill climbing and genetic algorithms (GAs), and the evaluation function of cost-based Mean Accuracy Gain (cMAG), which is provided by SVM classifiers, to find this optimal sequence. GA can find a good sequence because of the ability to escape from local optima.
| Original language | English (US) |
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| Number of pages | 1 |
| Journal | AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium |
| State | Published - 2008 |