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      KCI등재 SCOPUS

      Disturbed-entropy: A simple data quality assessment approach

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      https://www.riss.kr/link?id=A108570503

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      다국어 초록 (Multilingual Abstract)

      From the perspective of information value, we proposed a simple and effective approach to assess data quality, called disturbed-entropy. In specific, considering image classification task, the existing samples per category are statistically represente...

      From the perspective of information value, we proposed a simple and effective approach to assess data quality, called disturbed-entropy. In specific, considering image classification task, the existing samples per category are statistically represented as a pixel prototype, which is used to disturb the unseen samples. Then, the entropy of disturbed image is calculated based on predicted probability. Both the numerical and visual experiments are conducted to show the effect. In case of same data budget, the performance comparison based on selected good and bad data is significant and consistent. This work attempts to gain insight into data quality and redundancy.

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      참고문헌 (Reference) 논문관계도

      1 Y. Li, "Toward sustainability: Trade-offbetween data quality and quantity in crop pest recognition" 12 : 811241-, 2021

      2 Y. Zhao, "Stereoscopic video quality assessment in the context of internet of things" 10 : 2021

      3 Y. Li, "Semi-supervised few-shot learning approach for plant diseases recognition" 17 (17): 1-10, 2021

      4 Y. Yang, "Radar target recognition based on few-shot learning" 27 : 1-11, 2021

      5 J. Yang, "Noreference quality assessment for screen content images using visual edge model and AdaBoosting neural network" 30 : 6801-6814, 2021

      6 J. Yang, "No-reference quality evaluation of stereoscopic video based on spatio-temporal texture" 22 (22): 2635-2644, 2019

      7 Y. Li, "Meta-learning baselines and database for few-shot classification in agriculture" 182 : 106055-, 2021

      8 K. Sim, "MaD-DLS: mean and deviation of deep and local similarity for image quality assessment" 22 : 2020

      9 J. Yang, "MSTA-Net: Forgery detection by generating manipulation trace based on multi-scale self-texture attention" 31 : 2021

      10 Nwamaka U. Okafor ; Yahia Alghorani ; Declan T. Delaney, "Improving Data Quality of Low-cost IoT Sensors in Environmental Monitoring Networks Using Data Fusion and Machine Learning Approach" 한국통신학회 6 (6): 220-228, 2020

      1 Y. Li, "Toward sustainability: Trade-offbetween data quality and quantity in crop pest recognition" 12 : 811241-, 2021

      2 Y. Zhao, "Stereoscopic video quality assessment in the context of internet of things" 10 : 2021

      3 Y. Li, "Semi-supervised few-shot learning approach for plant diseases recognition" 17 (17): 1-10, 2021

      4 Y. Yang, "Radar target recognition based on few-shot learning" 27 : 1-11, 2021

      5 J. Yang, "Noreference quality assessment for screen content images using visual edge model and AdaBoosting neural network" 30 : 6801-6814, 2021

      6 J. Yang, "No-reference quality evaluation of stereoscopic video based on spatio-temporal texture" 22 (22): 2635-2644, 2019

      7 Y. Li, "Meta-learning baselines and database for few-shot classification in agriculture" 182 : 106055-, 2021

      8 K. Sim, "MaD-DLS: mean and deviation of deep and local similarity for image quality assessment" 22 : 2020

      9 J. Yang, "MSTA-Net: Forgery detection by generating manipulation trace based on multi-scale self-texture attention" 31 : 2021

      10 Nwamaka U. Okafor ; Yahia Alghorani ; Declan T. Delaney, "Improving Data Quality of Low-cost IoT Sensors in Environmental Monitoring Networks Using Data Fusion and Machine Learning Approach" 한국통신학회 6 (6): 220-228, 2020

      11 Y. Wang, "Generalizing from a few examples:A survey on few-shot learning" 53 (53): 1-34, 2020

      12 J. Ma, "Few-shot learning creates predictive models of drug response that translate from high-throughput screens to individual patients" 2 : 233-244, 2021

      13 X. Chao, "Few-shot imbalanced classification based on data augmentation" 27 : 1-9, 2021

      14 Y. Li, "Few-shot cotton pest recognition and terminal realization" 169 : 105240-, 2020

      15 L. Fu, "Fast and accurate detection of kiwifruit in orchard using improved YOLOv3-tiny model" 22 (22): 754-776, 2021

      16 Brainvendra Widi Dionova ; M.N. Mohammed ; S. Al-Zubaidi ; Eddy Yusuf, "Environment indoor air quality assessment using fuzzy inference system" 한국통신학회 6 (6): 185-194, 2020

      17 Y. Li, "Entropy-based redundancy analysis and information screening" 7 : 2021

      18 Y. Li, "Do we really need deep CNN for plant diseases identification?" 178 : 105803-, 2020

      19 Y. Li, "Distance-entropy: An effective indicator for selecting informative data" 1 : 818895-, 2022

      20 K. Sim, "Blind stereoscopic image quality evaluator based on binocular semantic and quality channels" 23 : 2021

      21 Y. Li, "ANN-based continual classification in agriculture" 10 (10): 178-, 2020

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