Earticle

초록

영어

Lung cancer is the most serious disease in the world and millions of people die of it every year. Because of the limitations of current treatment processes, it is difficult to cure lung cancer if the patient is no longer in the early stages. Therefore, it is necessary to diagnose lung cancer as early as possible, thereby increasing the chances to cure it. The Fuzzy Interactive Naïve Bayesian (FINB) network is a new Bayes network that can be used to classify lung cancer by using microarray data sets. The FINB network is an interactive network and every attribution has an interactive parent and with a weight on the relationship that shows the interaction of the attribution in the data set. In our experiments, we use the gene expression profiles from the Affymetrix Human Genome U133 Plus 2.0 microarray. We use the Neural Network with a Weighted Fuzzy Membership Function (NEWFM) to train the data set and reconstruct the Fuzzy Interactive Naïve Bayesian network. Then we compare the results with Tree augment naïve Bayesian (TAN) network. We conclude that the FINB network performs better than the TAN network.

목차

Abstract
 1. Introduction
 2. Materials and Method
  2.1. Materials
  2.2. Process of FINB Network Construction
  2.3. Preprocessing the Data Set
  2.4. Finding Interactive Parents and Calculating Weight
  2.5. Construction of an FINB Network
  2.6. Classification Using an FINB Network
 3. Results
 4. Conclusion
 Acknowledgements
 References

키워드

저자

• Zhenxing Zhang [ School of Information and Electrical Engineering, Ludong University, Shandong, China ]
• Letao Qu [ IT College, Gachon University, Seongnam, South Korea ]
• Joon S.Lim [ IT College, Gachon University, Seongnam, South Korea ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Bio-Science and Bio-Technology

• 간기

  격월간

• pISSN

  2233-7849

• 수록기간

  2009~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605