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Designing the rule classification with oversampling approach with high accuracy for imbalanced data in semiconductor production lines

  • 1213: Computational Optimization and Applications for Heterogeneous Multimedia Data
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A Correction to this article was published on 17 November 2022

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Abstract

The product quality is the major factor for enhancing the production ability and competitiveness. Decreasing the cost and increasing production capacity are common approaches to realize the enhancement of the product quality. The production managers apply various multimedia data to evaluate the product quality. For example, capturing the stam** sound to evaluate the correct cutting and taking the component image to measure the chip positions are common heterogeneous multimedia data that are applied to manufacturing. However, the production managers prefer to minimize the number of defective products, e. g. the secondary operation and fixing the product tolerance in the assembly stage, to fitting the production target. Therefore, contrasting the defective product identification procedure with high accuracy becomes a challenge due to the decrease of the number of the defective products. In this paper, we propose the Rule Classification with Oversampling (RCOS) approach to provide the high accuracy with few defective products. The proposed RCOS includes the oversampling technique and the rule classification approach to emphasize the properties of the defective products and provide the precise classes. Given few defective products, capturing the properties of the failure is difficult. The RCOS considers the revised Synthetic Minority Over-Sampling Technique (SMOTE) to highlight the failure properties, and then the rule model is considered to extract the root cause of the defective products. We implement the proposed RCOS in the semiconductor production line. From the experiment results, the proposed RCOS provide about at most 98% in accuracy, and the comparison shows that the results have been improved in common criteria e. g. the true-positive rate, G mean, F1 score, and False Alarm Rate. Therefore, the proposed RCOS provides high practicality for the implementation consideration.

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Funding

This study is conducted under the “III Innovative and Prospective Technologies Project (1/1)” of the Institute for Information Industry which is subsidized by the Ministry of Economic Affairs of the Republic of China.

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Authors and Affiliations

  1. Department of Mechanical Engineering, National Yang Ming Chiao Tung University, 1001 University Road, 300, Hsinchu, Taiwan, Republic of China

    Hsiao-Yu Wang & Ching-Hua Hung

  2. Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, No 57 Sec 2 Zhongshan Rd Tai** Dist, 41170, Taichung, Taiwan, Republic of China

    Chen-Kun Tsung

  3. Department of Computer Science and Engineering, National Chung Hsing University, 145 **ngda Rd South Dist, 402, Taichung, Taiwan, Republic of China

    Chen-Huei Chen

Authors
  1. Hsiao-Yu Wang
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  2. Chen-Kun Tsung
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Corresponding author

Correspondence to Chen-Kun Tsung.

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The original online version of this article was revised: Funding information was missing in the original publication of this article.

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Wang, HY., Tsung, CK., Hung, CH. et al. Designing the rule classification with oversampling approach with high accuracy for imbalanced data in semiconductor production lines. Multimed Tools Appl 81, 36437–36452 (2022). https://doi.org/10.1007/s11042-021-11552-1

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