Skip to main content
Article thumbnail
Location of Repository

Classifying Weak, and Strong Components using ROC Analysis with Application to Burn-in

By Shaomin Wu and Min Xie


Any population of components produced might be composed of two sub-populations: weak components are less reliable, and deteriorate faster whereas strong components are more reliable, and deteriorate slower. When selecting an approach to classifying the two sub-populations, one could build a criterion aiming to minimize the expected mis-classification cost due to mis-classifying weak (strong) components as strong (weak). However, in practice, the unit mis-classification cost, such as the cost of mis-classifying a strong component as weak, cannot be estimated precisely. Minimizing the expected mis-classification cost becomes more difficult. This problem is considered in this paper by using ROC (Receiver Operating Characteristic) analysis, which is widely used in the medical decision making community to evaluate the performance of diagnostic tests, and in machine learning to select among categorical models. The paper also uses ROC analysis to determine the optimal time for burn-in to remove the weak population. The presented approaches can be used for the scenarios when the following information cannot be estimated precisely: 1) life distributions of the sub-populations, 2) mis-classification cost, and 3) proportions of sub-populations in the entire population

Topics: HA33
Publisher: IEEE
Year: 2007
DOI identifier: 10.1109/tr.2007.897073
OAI identifier:

Suggested articles


  1. (2006). A change-point analysis for modeling incomplete burn-in for light displays,”
  2. (1959). A graphical estimation of mixed weibull parameters in life testing of electron tubes,”
  3. (1983). A method of comparing the areas under receiver operating characteristic curves derived from the same cases,”
  4. (2005). A scored metric for classifier evaluation and selection,” in ROCML,
  5. (2006). An extended model for optimal burn-in procedures,”
  6. (1997). An introduction to reliability and maintainability engineering. McGraw-Hill Companies,
  7. (1995). Bathtub failure rate and upside-down bathtub mean residual life,”
  8. (1993). Bayesian analysis of the mixture of exponential failure distributions,”
  9. (2001). Bayesian calculation of cost optimal burn-in test durations for mixed exponential populations,” Reliability Engineering and System Safety,
  10. (2002). Determination of burn-in parameters and residual life for highly reliable products,”
  11. (1989). Diagonising diagnoses receiver operating characteristic methods and psychiatry,”
  12. (1978). Estimation in the piece-wise constant harzard rate model when the data are grouped,”
  13. (2004). Minimizing cost-functions related to both burn-in and field-operation under a generalized model,”
  14. (2005). On optimal burn-in procedures - a generalized model,”
  15. (1998). Optimal burn-in for minimizing cost and multi-objectives,”
  16. (2004). Optimal burn-in policy by using an integrated wiener process,”
  17. (2001). Optimal burn-in time for highly reliable products,”
  18. (1993). Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine,”
  19. (1991). Reliability in NDT: ROC study of radiographic weld inspections,”
  20. (2005). Repairing concavities in ROC curves,”
  21. (2001). Robust classification for imprecise environments,”
  22. (1966). Signal detection theory and psychophsics.
  23. (1975). Signal detection theory and ROC analysis.
  24. (2005). Some consideration on system burn-in,”
  25. (1989). Some practical issues of experimental design and data analysis

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.