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Classification of LED Packages for Quality Control by Discriminant Analysis, Neural Network and Decision Tree.

Heesoo Shim1, Sun Kyoung Kim1

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Supervised learning enhances LED classification. A binary decision tree achieved 99.4% accuracy for quality control, outperforming discriminant analysis and neural networks in efficiency and effectiveness.

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LEDdecision treediscriminant analysismachine learningneural network

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Area of Science:

  • Electrical Engineering
  • Machine Learning
  • Quality Control

Background:

  • Accurate classification of Light Emitting Diodes (LEDs) is crucial for manufacturing quality control.
  • Traditional methods may not achieve the desired accuracy or efficiency in identifying defective LEDs.

Purpose of the Study:

  • To investigate the efficacy of supervised learning techniques for improving LED classification.
  • To compare the performance of discriminant analysis, neural networks, and decision trees for LED quality control.

Main Methods:

  • Development of a dedicated hardware system for LED testing and data acquisition.
  • Acquisition and analysis of electrical and optical data from LEDs.
  • Implementation and comparison of discriminant analysis, neural network, and binary decision tree classification models.

Main Results:

  • Discriminant analysis yielded a 77.9% true positive rate, insufficient for quality control.
  • Neural network learning improved the true positive rate to 97.8%, but a 2.2% false negative rate persisted.
  • A binary decision tree achieved a 99.4% true positive rate with high efficiency (14 splits and 8.2s training time).

Conclusions:

  • The binary decision tree demonstrates superior performance for LED classification compared to discriminant analysis and neural networks.
  • The decision tree offers a highly effective and efficient solution for automated LED quality control.
  • This study highlights the advantages of decision tree algorithms in achieving high accuracy and efficiency in product classification.