Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Self-organizing maps
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
How Genetic Algorithms Work: A Critical Look at Implicit Parallelism
Proceedings of the 3rd International Conference on Genetic Algorithms
Self-Organizing Map Based on Block Learning
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part IV
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Data detection based on self organizing maps is presented for hematopoietic tumor patients. Learning data for the maps are generated from the screening data of examinees. The incomplete screening data without some item values is then supplemented by substituting averaged non-missing item values. In addition, redundant items, which are common to all the data and tend to have an unfavorable influence on data detection, are eliminated by a genetic algorithm and/or an immune algorithm. It is basically judged, by observing the label of a winner neuron in the map, whether the data presented to the map belongs to the class of hematopoietic tumors. Some experimental results are provided to show that the proposed methods achieve the high probability of correctly identifying examinees as hematopoietic tumor patients.