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< Variation aware marginal defects detection

Application of Adaptive Adjustment Classification Algorithm in Early Life Failure Test

Kategorie: Open Seminar - Rechnerarchitektur

10:30-11:30, ITI-Seminarraum 3.175, Ph.D. candidate Tai Song, Institut für Technische Informatik

In manufacturing test Data Mining, researchers usually overlook the importance of distinguish on process variation defects and marginal defects. It can seriously affect the result of the Early Life Failure (ELF). Theoretically, we use a classifier to identify these two defects. the majority of classifier assumed that the distribution of the data is relatively balanced&#65292;but in fact, using a single or fixed classifier often leads to inaccurate predictions. The classifier needs to adjust changes according to defect characteristics or use different classifiers to achieve maximum accuracy. This study will explain about classifier adaptive adjustment mechanism according to the defect feature. Adaptive adjustment method provides balancing mechanism for the dataset's distribution, so that the classification results will be enhanced in terms of classification performance. The adaptive adjustment methods produce the accuracy, sensitivity, specificity, and g-mean score as high as  %,  %,  %,  % respectively. Hence, the adaptive adjustment methods can be a viable solution for imbalanced class on manufacturing test dataset.


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