  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
