Avtor/Urednik		Šprogar, Matej; Kokol, Peter; Alayon, Silvia
Naslov		Autonomous evolutionary alghorithm in medical data analysis
Tip		članek
Vir		In: Kokol P, Stiglic B, Zorman M, et al, editors. Proceedings of the 15th IEEE symposium on computer-based medical systems (CBMS 2002); 2002 Jun 4-7; Maribor. Los Alamitos: Institute of electrical and electronics engineers,
Leto izdaje		2002
Obseg		str. 71-6
Jezik		eng
Abstrakt		A novel autonomous evolutionary algorithm for construction of decision trees is presented together with nn analysis of different medical datasets. The algorithm's capability to sedfadapt to n given problem is used as a measure to predict if some dataset is just difficult or impossible to analyze. If a specific dntaset doesn't include enougia or proper information for a creation of a good general decision model then the overfittirag will occur. To detect overfitting we can use several existing techniques, the most common uses special testing data that is excluded from the learning phase. The autonomous algorithm on average produces very general solutions or gives. no solution if the dataset is prone to overfitting.
Deskriptorji		DECISION TREES DIAGNOSIS, COMPUTER-ASSISTED