The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
우리는 SELDI-TOF 질량 스펙트럼 데이터세트의 특징 선택을 위한 새로운 필터 방법을 제안합니다. 이 방법에서는 개수를 기준으로 샘플의 분포를 고려하여 특징의 우수성을 나타내는 새로운 관련성 지수를 정의했습니다. 관련성 지수는 분류를 위한 특징 세트를 얻기 위해 사용될 수 있습니다. 우리의 방법은 매우 높은 차원의 질량 스펙트럼 데이터 세트에 적용할 수 있으며 간단한 샘플 계산을 기반으로 하기 때문에 허용 가능한 계산 시간 내에 실제 크기의 임상 데이터 세트를 처리할 수 있습니다. 새로운 방법은 세 가지 공개 질량 스펙트럼 데이터 세트에 적용되었으며 기존 필터 방법보다 우수하거나 비슷한 결과를 보여주었습니다.
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Trung-Nghia VU, Syng-Yup OHN, "A Filter Method for Feature Selection for SELDI-TOF Mass Spectrum" in IEICE TRANSACTIONS on Information,
vol. E92-D, no. 2, pp. 346-348, February 2009, doi: 10.1587/transinf.E92.D.346.
Abstract: We propose a new filter method for feature selection for SELDI-TOF mass spectrum datasets. In the method, a new relevance index was defined to represent the goodness of a feature by considering the distribution of samples based on the counts. The relevance index can be used to obtain the feature sets for classification. Our method can be applied to mass spectrum datasets with extremely high dimensions and process the clinical datasets with practical sizes in acceptable calculation time since it is based on simple counting of samples. The new method was applied to the three public mass spectrum datasets and showed better or comparable results than conventional filter methods.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E92.D.346/_p
부
@ARTICLE{e92-d_2_346,
author={Trung-Nghia VU, Syng-Yup OHN, },
journal={IEICE TRANSACTIONS on Information},
title={A Filter Method for Feature Selection for SELDI-TOF Mass Spectrum},
year={2009},
volume={E92-D},
number={2},
pages={346-348},
abstract={We propose a new filter method for feature selection for SELDI-TOF mass spectrum datasets. In the method, a new relevance index was defined to represent the goodness of a feature by considering the distribution of samples based on the counts. The relevance index can be used to obtain the feature sets for classification. Our method can be applied to mass spectrum datasets with extremely high dimensions and process the clinical datasets with practical sizes in acceptable calculation time since it is based on simple counting of samples. The new method was applied to the three public mass spectrum datasets and showed better or comparable results than conventional filter methods.},
keywords={},
doi={10.1587/transinf.E92.D.346},
ISSN={1745-1361},
month={February},}
부
TY - JOUR
TI - A Filter Method for Feature Selection for SELDI-TOF Mass Spectrum
T2 - IEICE TRANSACTIONS on Information
SP - 346
EP - 348
AU - Trung-Nghia VU
AU - Syng-Yup OHN
PY - 2009
DO - 10.1587/transinf.E92.D.346
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E92-D
IS - 2
JA - IEICE TRANSACTIONS on Information
Y1 - February 2009
AB - We propose a new filter method for feature selection for SELDI-TOF mass spectrum datasets. In the method, a new relevance index was defined to represent the goodness of a feature by considering the distribution of samples based on the counts. The relevance index can be used to obtain the feature sets for classification. Our method can be applied to mass spectrum datasets with extremely high dimensions and process the clinical datasets with practical sizes in acceptable calculation time since it is based on simple counting of samples. The new method was applied to the three public mass spectrum datasets and showed better or comparable results than conventional filter methods.
ER -