Earticle

초록

영어

The need of similarity measures in life science is ever paramount given the modern biotechnology in producing and storing biomedical datasets in large amounts. This paper presents a novel scheme in measuring similarity of two datasets by prediction class, namely SPC. SPC offers an alternative approach to traditionally used ones such as pairwise correlations which assume every attribute carries equal importance. The unique advantage of SPC is the use of a machine learning model called Fuzzy Unordered Rule Induction to infer the similarity between two datasets based on their common attributes and their degrees of relevance pertaining to a predicted class. The method is demonstrated by a case of comparing lung cancer dataset and heart disease dataset.

목차

Abstract
 1. Introduction
 2. SPC Method
  2.1. Data-preprocessing
  2.2. Classification Model Induction
  2.3. Rule Analysis
 3. Experiment
 4. Conclusions
 Acknowledgements
 References

키워드

저자

• Simon Fong [ Department of Computer and Information Science University of Macau, Macau SAR ]
• Osama Mohammed [ Department of Computer Science, University of Victoria, BC, Canada ]
• Jinan Fiaidhi [ Department of Computer Science Lakehead University, Thunder Bay, Canada ]
• Sabah Mohammed [ Department of Computer Science Lakehead University, Thunder Bay, Canada ]
• Chee Keong Kwoh [ Biomedical Engineering Research Centre (BMERC) Nanyang Technological University, Republic of Singapore ]

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