Classification on defective items using unidentified samples

Li, Tze Fen; Chang, Shui-Ching

doi:10.1016/j.patcog.2004.05.008

Cited by 4 publications

(3 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…d n ðX n Þ decides c i if X 2 À i,n and hence they depend on the existing observations X n : The a.o. classifier which minimizes (7) can be found in the same way as the Bayes decision ruled n [11], which can be expressed as follows.…”

Section: The Eb Decision Rule and Stochasticmentioning

confidence: 99%

“…(2) Set hðjx n Þ ¼ 1 for each point in S 1 before input any unidentified sample Equation (14) is a stable stochastic approximation which does not produce a large computation error [11]. It is used to compute E ½jX n , and the simulations are presented to demonstrate the accuracy of the estimation procedure.…”

Section: Stochastic Approximationmentioning

confidence: 99%

“…There are two approaches for estimating the defective percentage using the unclassified lifetime sample. One approach needs sufficient information about the conditional density functions for good and defective classes [11]. It can construct an empirical Bayes (EB) decision rule that, when the number of unidentified samples increases, converges the recognition rate of the EB classifier to the rate of the Bayes decision rule.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Two algorithms for classification on quality control

Chang

2010

Journal of the Chinese Institute of Industrial Engineers

Self Cite

View full text Add to dashboard Cite

In this article, no probability density and no prior probability are assumed for the good and defective classes, except possibly two means. A sequence of unidentified input lifetimes is used to estimate the percentages of two classes. After the classifier system is put into use, the new coming unidentified input lifetime can improve the percentages. A Monte Carlo simulation study with exponential or Weibull or gamma distribution on life is presented to demonstrate the classification. The study can be widely used in quality control.

show abstract

Section: The Eb Decision Rule and Stochasticmentioning

confidence: 99%

Section: Stochastic Approximationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Two algorithms for classification on quality control

Chang

2010

Journal of the Chinese Institute of Industrial Engineers

Self Cite

View full text Add to dashboard Cite

show abstract

Evaluation of methods for reduction of surface texture features

Stachowiak

Podsiadło

2006

Tribol Lett

View full text Add to dashboard Cite

The diagnosis of worn and damaged surfaces is an important issue in machine failure analysis and condition monitoring. Of many approaches used, image classification based on feature parameters has often proven to be particularly useful. However, large image databases can be computationally costly to analyse, and the datasets are susceptible to noise. Hence, it is essential to determine which feature parameters hold the most useful information, in order to improve the classification rate and computation time. This paper presents a performance evaluation of dimension reduction techniques currently used in pattern recognition. A comparison of three methods is conducted, in order to determine which is able to produce the best results over a large range of image datasets. The methods analysed are: Non-Linear Fisher, Principal Component Analysis and kernel Principal Component Analysis. These are then tested against four different classifiers to obtain the best combination. These classifiers are: Linear Discriminant Classifier, Quadratic Discriminant Classifier, k-Nearest Neighbour and Support Vector Machine Classifiers. For further analysis, two combined dimension reduction and classification methods are tested: Minimum Classification Error and reduced feature space Support Vectors. For the comparison, four datasets of images with different scales and rotations are used, i.e. Brodatz textures, artificially generated isotropic fractal images and Talysurf images of sandblasted and abraded steel surfaces. The results showed that a combination of the Non-Linear Fisher dimension reduction technique and a Linear Support Vector Machine Classifier gave the best performance overall and are the most promising for the application in automated machine condition monitoring and expert free failure analysis. Further improvement can be achieved by performing a step-wise dimension reduction by first reducing the features using the Principal Component Analysis method, then further reduction with the Non-Linear Fisher technique.

show abstract

Empirical Bayes decision rule for classification on defective items in Weibull distribution

Chang

2006

Applied Mathematics and Computation

View full text Add to dashboard Cite

Classification on defective items using unidentified samples

Cited by 4 publications

References 27 publications

Two algorithms for classification on quality control

Two algorithms for classification on quality control

Evaluation of methods for reduction of surface texture features

Empirical Bayes decision rule for classification on defective items in Weibull distribution

Contact Info

Product

Resources

About