IEICE TRANSACTIONS on Fundamentals
Self-Organizing Map Based Data Detection of Hematopoietic Tumors
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E90-A No.6 pp.1170-1179
- Publication Date
- 2007/06/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1093/ietfec/e90-a.6.1170
- Type of Manuscript
- PAPER
- Category
- Nonlinear Problems
Authors
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Akitsugu OHTSUKA, Hirotsugu TANII, Naotake KAMIURA, Teijiro ISOKAWA, Nobuyuki MATSUI, "Self-Organizing Map Based Data Detection of Hematopoietic Tumors" in IEICE TRANSACTIONS on Fundamentals,
vol. E90-A, no. 6, pp. 1170-1179, June 2007, doi: 10.1093/ietfec/e90-a.6.1170.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1093/ietfec/e90-a.6.1170/_p
Copy
@ARTICLE{e90-a_6_1170,
author={Akitsugu OHTSUKA, Hirotsugu TANII, Naotake KAMIURA, Teijiro ISOKAWA, Nobuyuki MATSUI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Self-Organizing Map Based Data Detection of Hematopoietic Tumors},
year={2007},
volume={E90-A},
number={6},
pages={1170-1179},
abstract={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.},
keywords={},
doi={10.1093/ietfec/e90-a.6.1170},
ISSN={1745-1337},
month={June},}
Copy
TY - JOUR
TI - Self-Organizing Map Based Data Detection of Hematopoietic Tumors
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1170
EP - 1179
AU - Akitsugu OHTSUKA
AU - Hirotsugu TANII
AU - Naotake KAMIURA
AU - Teijiro ISOKAWA
AU - Nobuyuki MATSUI
PY - 2007
DO - 10.1093/ietfec/e90-a.6.1170
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E90-A
IS - 6
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - June 2007
AB - 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.
ER -
English
