[M-AG39] 海洋地球インフォマティクス

[MAG39-05] Classification with imbalanced cloud data using deep convolutional neural network

*松岡 大祐^1,2、中野 満寿男¹、杉山 大祐¹、内田 誠一³ (1.海洋研究開発機構、2.科学技術振興機構、3.九州大学)

キーワード：ディープラーニング、クラス分類、熱帯低気圧

Image classification using deep convolutional neural network is effective technique to detect extreme phenomenafrom climate data. However, the number of extreme phenomena such as tropical cyclone (TC) is overwhelmingly small compared with others. It is knon that classification performance decline du to this imbalance between positive (TCs) and negative examples (non-TCs). In the present study, we developed a new negative data selection method for binary classification for cloud images. We analyze the relationship between the ease of classification and the characteristics of data, and optimize the training data in order to decrease the false alarm ratio (FAR). As the results, we succeeded in decrease FAR from 32.8–53.4% to 60.0–70.0% in the western North Pacific in the period from July to November.