2020/01/31 by Toshitaka Hayashi, Hamido Fujita · 34 citations
Computer Science · Mathematics · Medicine · #Anomaly Detection Techniques and Applications #Artificial intelligence #Binary data #Binary number #COVID-19 diagnosis using AI #Class (philosophy) #Cluster (spacecraft) #Cluster analysis #Computer science #Data mining #Domain Adaptation and Few-Shot Learning #Machine learning #Mathematics #Multivariate statistics #Pattern recognition (psychology) #Shot (pellet) #Task (project management) #Training (meteorology) #Training set #Zero (linguistics) #cs.LG #stat.ML
paper · pdf · doi:10.1007/s12652-020-02268-5
published in Journal of Ambient Intelligence and Humanized Computing 12(2), 1897-1911 (Springer Science+Business Media) · J Ambient Intell Human Comput (2020)
openalex publication_date 2020/06/28 · arxiv created 2020/07/01 · arxiv updated 2020/07/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
Supervised learning requires a sufficient training dataset which includes all label. However, there are cases that some class is not in the training data. Zero-Shot Learning (ZSL) is the task of predicting class that is not in the training data(target class). The existing ZSL method is done for image data. However, the zero-shot problem should happen to every data type. Hence, considering ZSL for other data types is required. In this paper, we propose the cluster-based ZSL method, which is a baseline method for multivariate binary classification problems. The proposed method is based on the assumption that if data is far from training data, the data is considered as target class. In training, clustering is done for training data. In prediction, the data is determined belonging to a cluster or not. If data does not belong to a cluster, the data is predicted as target class. The proposed method is evaluated and demonstrated using the KEEL dataset. This paper has been published in the Journal of Ambient Intelligence and Humanized Computing. The final version is available at the following URL: https://link.springer.com/article/10.1007/s12652-020-02268-5