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- W2373216917 abstract "Defect recognition based on image information is one of the key technologies in strip steel surface automatic inspection system.Applying a single type of classification technology or building classifier in one step,image classification of complex strip steel defects is very difficult to achieve the desire result.In this paper,a combination classifier based on Boosting algorithm and SLIQ decision tree was presented.With adaptive weight updating and weighted voting,Boosting algorithm could build and combine multiple functional complementary classifiers,and improve recognition performance effectively compared with a single classifier.Through data pre-sorting and breadth-first growing methods,SLIQ decision tree was fit for large data processing and could be used in weak learning algorithm of each single classifier.Tests on real strip steel surface defect datasets were carried out.The result shows that combination classifiers of Boosting optimized SLIQ decision tree can improve the accuracy of defects recognition up to 90%." @default.
- W2373216917 created "2016-06-24" @default.
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- W2373216917 date "2010-01-01" @default.
- W2373216917 modified "2023-09-24" @default.
- W2373216917 title "Strip steel surface defect recognition based on Boosting optimized decision tree" @default.
- W2373216917 hasPublicationYear "2010" @default.
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