Cluster-based zero-shot learning for multivariate data

Supervised learning requires a sufficient training dataset which includes all labels. 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 (unseen 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 unseen 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 unseen class. The proposed method is evaluated and demonstrated using the KEEL datasets.