Self-Organizing Map Based Data Detection of Hematopoietic Tumors(Nonlinear Problems)

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概要

• 論文の詳細を見る

• 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.

• 社団法人電子情報通信学会の論文
• 2007-06-01

著者

• Kamiura N

  Graduate School Of Engineering University Of Hyogo

• Ohtsuka Akitsugu

  Yamato Scale Co. Ltd.

• TANII Hirotsugu

  Fujitsu Ten Ltd.

• ISOKAWA Teijiro

  Graduate School of Engineering, University of Hyogo

• OHTSUKA Akitsugu

  Division of Computer Engineering, University of Hyogo

• TANII Hirotsugu

  Division of Computer Engineering, University of Hyogo

• KAMIURA Naotake

  Division of Computer Engineering, University of Hyogo

• ISOKAWA Teijiro

  Division of Computer Engineering, University of Hyogo

• MATSUI Nobuyuki

  Division of Computer Engineering, University of Hyogo

• Matsui N

  Graduate School Of Engineering University Of Hyogo

• Isokawa Teijiro

  Graduate School Of Engineering University Of Hyogo

関連論文

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• Design and Fault Masking of Two-Level Cellular Arrays on Multiple-Valued Logic
• On Ternary Cellular Arrays Designed from Ternary Decision Diagrams
• Design of Repairable Cellular Arrays on Multiple-Valued Logic
• Design of a Multiple-Valued Cellular Array (Special Issue on Multiple-Valued Integrated Circuits)
• Classification for data of hematopoietic tumor patients with fast block-matching-based self-organizing map learning in dynamic environments (特集 医用システム)
• Self-Organizing Map Based Data Detection of Hematopoietic Tumors(Nonlinear Problems)
• Self-Organizing Map Based on Block Learning(Nonlinear Problems)
• A Self-Organizing Map Approach for Detecting Confusion between Blood Samples
• On Fault-Tolerant Fuzzy Controllers Based on Shifting Fuzzy Variables

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