政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/126578

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/126578

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政大機構典藏 > 理學院 > 應用數學系 > 學位論文 > Item 140.119/126578

請使用永久網址來引用或連結此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/126578

題名:	深度學習在不平衡數據集之研究 Survey on Deep Learning with Imbalanced Data Sets
作者:	蔡承孝 Tsai, Cheng-Hsiao
貢獻者:	蔡炎龍蔡承孝 Tsai, Cheng-Hsiao
關鍵詞:	深度學習卷積神經網路不平衡數據集異常偵測圖像分類 Deep Learning CNN Imbalanced Data Sets Anomaly Detection Image Classification
日期:	2019
上傳時間:	2019-10-03 17:17:29 (UTC+8)
摘要:	本文旨在回顧利用深度學習處理不平衡數據集和異常偵測的方法，我們從 MNIST 生成兩個高度不平衡數據集，不平衡比率高達 2500 並應用在多元分類任務跟二元分類任務上，在二元分類任務中第 0 類為少數類；而在多元分類任務中少數類為第 0、1、4、6、7 類，我們利用卷積神機網路來訓練我們的模型。在異常偵測方面，我們用預先訓練好的手寫辨識 CNN 模型來判斷其他 18 張貓狗的圖片是否為手寫辨識圖片。由於數據的高度不平衡，原始分類模型的表現不盡理想。因此，在不同的分類任務上，我們分別利用 6 個和 7 個不同的方法來調整我們的模型。我們發現新的損失函數 Focalloss 在多元分類任務表現最好，而在二元分類任務中隨機過採樣的表現最佳，但是成本敏感學習的方法並不適用於我們所生成的不平衡數據集。我們利用信心估計讓分類器成功判斷所有貓狗圖片皆不是手寫辨識圖片。 This paper is a survey on deep learning with imbalanced data sets and anomaly detection. We create two imbalanced data sets from MNIST for multi-classification task with minority classes 0,1,4,6,7 and binary classification task with minority class 0. Our data sets are highly imbalanced with imbalanced rate ρ = 2500 and we use convolutional neural network(CNN) for training. In anomaly detection,we use the pretrained CNN handwriting classifier to decide the 18 cat and dog pictures are handwriting pictures or not. Due to the data set is imbalanced, the baseline model have poor performance on minority classes. Hence, we use 6 and 7 different methods to adjust our model. We find that the focal loss function and random over-sampling(ROS) have best performance on multi-classification task and binary classification task on our imbalanced data sets but the cost sensitive learning method is not suitable for our imbalanced data sets. By confidence estimation, our classifier successfully judge all the pictures of cat and dog are not handwriting picture.
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Training cost-sensitive neural networks with methods addressing the class imbalance problem. IEEE Transactions on Knowledge & Data Engineering, (1):63–77, 2006.
描述:	碩士國立政治大學應用數學系 105751009
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0105751009
資料類型:	thesis
DOI:	10.6814/NCCU201901175
顯示於類別:	[應用數學系] 學位論文

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