In the last two decades, there has been significant advancement in heuristics for inducing Bayesian belief networks for the purpose of automatic distillation of knowledge from masses of data with target concepts. However, there are various circumstances where we are confronted to fix a set of most influencing variables in modeling of class variable. This arises in provision of confidence measures on set of variables used in the structure learning of data. In this study, we have tweaked empirical as well as theoretical aspects of various feature selection evaluators, their corresponding searching methods under six well known scoring functions in K2 which is a notable structure learning technique in Bayesian belief network. We have come up with some useful findings for overall computationally efficient approach among eleven evaluators. This analysis is useful in inducing better structure from the given dataset in imparting improved performance metric useful in the domain of control and automation.
목차
Abstract 1. Introduction 2. Motivation 3. Literature Review 4. Bayesian Belief Network Scoring Function 5. Feature Selection Evaluators 5.1. Feature Reduction 5.2. Feature Ranking 5.3. Feature Subset Selection 6. Experimental Setup 7. Result and Discussion 8. Conclusion References
보안공학연구지원센터(IJCA) [Science & Engineering Research Support Center, Republic of Korea(IJCA)]
설립연도
2006
분야
공학>컴퓨터학
소개
1. 보안공학에 대한 각종 조사 및 연구
2. 보안공학에 대한 응용기술 연구 및 발표
3. 보안공학에 관한 각종 학술 발표회 및 전시회 개최
4. 보안공학 기술의 상호 협조 및 정보교환
5. 보안공학에 관한 표준화 사업 및 규격의 제정
6. 보안공학에 관한 산학연 협동의 증진
7. 국제적 학술 교류 및 기술 협력
8. 보안공학에 관한 논문지 발간
9. 기타 본 회 목적 달성에 필요한 사업
간행물
간행물명
International Journal of Control and Automation
간기
월간
pISSN
2005-4297
수록기간
2008~2016
십진분류
KDC 505DDC 605
이 권호 내 다른 논문 / International Journal of Control and Automation Vol.8 No.3