Early Criticality Prediction Model Using Fuzzy Classification 


Vol. 7,  No. 5, pp. 1401-1408, May  2000
10.3745/KIPSTE.2000.7.5.1401


PDF
  Abstract

Criticality prediction models that determine whether a design entity is fault-prone or non fault-prone play an important role in reducing system development cost because the problems in early phases largely affect the quality of the late products. Real-time systems such as telecommunication systems are so large that criticality prediction is more important in real-time system design. The current models are based on the technique such as discriminant analysis, neural net and classification trees. These models have some problems with analyzing cause of the prediction results and low extendability. In this paper, we propose a criticality prediction model using fuzzy rulebase constructed by genetic algorithm. This model makes it easy to analyze the cause of the result and also provides high extendability, high applicability, and no limit on the number of rules to be found.

  Statistics
  Cite this article

[IEEE Style]

E. S. Hong and Y. K. Kwon, "Early Criticality Prediction Model Using Fuzzy Classification," The Transactions of the Korea Information Processing Society (1994 ~ 2000), vol. 7, no. 5, pp. 1401-1408, 2000. DOI: 10.3745/KIPSTE.2000.7.5.1401.

[ACM Style]

Euy Seok Hong and Yong Kil Kwon. 2000. Early Criticality Prediction Model Using Fuzzy Classification. The Transactions of the Korea Information Processing Society (1994 ~ 2000), 7, 5, (2000), 1401-1408. DOI: 10.3745/KIPSTE.2000.7.5.1401.

Forms

Search
(IN TITLE, AUTHOR, ABSTRACT,KEYWORDS)

Advanced Search ▼

POPULAR KEYWORDS
(TOP 10 KEYWORDS)

Recent Publications
(LAST 3 YEARS)

Old Journals

Indexing

All publications of TKIPS is indexed in DOI, EBSCO, Google Scholar, Crossref, and CrossCheck.

TKIPS is also selected as the Journal for Accreditation by NRF (National Research Foundation of Korea).

Related Journals