论文标题
基于群集的零局学习用于多元数据
Cluster-based Zero-shot learning for multivariate data
论文作者
论文摘要
监督学习需要一个足够的培训数据集，其中包括所有标签。但是，在某些情况下，某些课程不在培训数据中。零射击学习（ZSL）是预测不在培训数据（目标类）中的类的任务。现有的ZSL方法是为图像数据完成的。但是，每种数据类型都应发生零射击问题。因此，考虑其他数据类型的ZSL。在本文中，我们提出了基于群集的ZSL方法，这是用于多元二进制分类问题的基线方法。提出的方法是基于以下假设：如果数据远离训练数据，则将数据视为目标类。在培训中，用于培训数据进行聚类。在预测中，数据是确定属于集群的。如果数据不属于群集，则将数据预测为目标类。使用龙骨数据集评估和证明所提出的方法。本文已发表在《环境智能和人性化计算》杂志上。最终版本可在以下URL上找到：https：//link.springer.com/article/10.1007/s12652-020-020-02268-5
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
