유전자 알고리즘과 Feature Wrapping을 통한 마이크로어레이 데이타 중복 특징 소거법 = Removing Non-informative Features by Robust Feature Wrapping Method for Microarray Gene Expression Data

다국어 초록 (Multilingual Abstract)

Due to the high dimensional problem, typically machine learning algorithms have relied on feature selection techniques in order to perform effective classification in microarray gene expression datasets. However, the large number of features compared to the number of samples makes the task of feature selection computationally inprohibitive and prone to errors. One of traditional feature selection approach was feature filtering; measuring one gene per one step. Then feature filtering was an univariate approach that cannot validate multivariate correlations. In this paper, we proposed a function for measuring both class separability and correlations. With this approach, we solved the problem related to feature filtering approach.

국문 초록 (Abstract)

본 논문에서는 유전자 사이의 상관계수가 높은 마이크로어레이 데이타에 대하여 제안하는 알고리즘을 통해 상관계수가 낮은 유전자들의 부집합을 만들고, 이에 대해 적합 함수를 통한 평가로 기존 방법론이 가지는 한계를 극복할 수 있도록 하였다. 기존 방법론은 개별 특징의 평가를 통해 중복 특징을 제거하며, 상관계수에 대한 고려가 없어 선택된 유전자 부집합들의 상관계수가 높은 문제가 있었다. 이에 따라 제안하는 알고리즘은 특징간의 관계를 평가하는 Feature Wrapping 기법을 활용하여, 추출된 유전자 부집합에 포함된 유전자 사이의 상관관계가 낮고, 클래스 구분력이 높은 특징을 갖도록 하였다.

목차 (Table of Contents)

• 요약
• Abstract
• 1. 서론
• 2. 선행 연구
• 3. 제안하는 알고리즘
• 4. 실험
• 5. 결론
• 참고문헌

참고문헌 (Reference)

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5 Kevin Dobbin, "Sample size determination in microarray experiments for class comparison and prognostic classification" 6 : 27-, 2005

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8 Carla S. Möller-Levet, "Exploiting sample variability to enhance multivariate analysis of microarray data" 23 : 2733-2740, 2007

9 Seo Young Kim, "Comparison of various statistical methods for identifying differential gene expression in replicated microarray data" 15 (15): 2006

10 Ian A. Wood, "Classification based upon gene expression data: bias and precision of error rates" 23 : 1363-1370, 2007

11 Yudi Pawitan, "Bias in the estimation of false discovery rate in microarray studies" 21 : 3865-, 2005

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14 Dan Nettleton, "A Discussion of Statistical Methods for Design and Analysis of Microarray Experiments for Plant Scientists" 18 : 2112-2121, 2006

15 Cianluca B, "A Blocking Startegy to Improve Gene Selection for Classification of Gene Expression Data" 293-300, 2007