특징 선택을 위한 혼합형 유전 알고리즘과 분류 성능 비교 = Hybrid Genetic Algorithms for Feature Selection and Classification Performance Comparisons

국문 초록 (Abstract)

이 논문은 특징 선택을 위한 새로운 혼합형 유전 알고리즘을 제안한다. 탐색을 미세 조정하기 위한 지역 연산을 고안하였고, 이들 연산을 유전 알고리즘에 삽입하였다. 연산의 미세 조정 강도를 조절할수 있는 매개 변수를 설정하였으며, 이 변수에 따른 효과를 측정하였다. 다양한 표준 데이타 집합에 대해 실험한 결과, 제안한 혼합형 유전 알고리즘이 단순 유전 알고리즘과 순차 탐색 알고리즘에 비해 우수함을 확인하였다.

다국어 초록 (Multilingual Abstract)

This paper proposes a novel hybrid genetic algorithm for the feature selection. Local search operations are devised and embedded in hybrid GAs to fine-tune the search. The operations are parameterized in terms of the fine-tuning power, and their effectiveness and timing requirement are analyzed and compared. Experimentations performed with various standard datasets revealed that the proposed hybrid GA is superior to a simple GA and sequential search algorithms.

목차 (Table of Contents)

• 요약
• Abstract
• 1. 서론
• 2. 특징 선택을 위한 단순 유전 알고리즘
• 3. 혼합형 유전 알고리즘
• 4. 실험과 토론
• 5. 결론
• 참고문헌
• 저자소개

참고문헌 (Reference)

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