Active Learning with Numerical Feature Annotation
Zhiye Fu, Hsing-Kuo Pao, Jiabin He · 2021 IEEE International Conference on Big Data (Big Data) · 2021
Active learning is an algorithm that aims to minimize the labeling effort in model training by wisely choosing the key data for labeling in an iterative and alternately querying labeled data and training procedure. While most of studies focus on the query strategy such as selecting the informative instances for labeling in each run, some others consider the query on features, or the query on both of the features and instances simultaneously to build effective model more efficiently than before. A typical strategy of querying features can take care only the categorical features. In this work, we extend the possibility of querying numerical features as well. We consider a Vector Quantization (VQ) technique to transform the numerical features to categorical ones and then all the features, as well as instances can all be evaluated using a unified criterion in the feature/instance selection step. Moreover, we study multivariate VQ as well as univariate VQ to find the best approach for the qunatization before sending the numerical features to categorical ones. After all, we can have active learning that deals with the selection on informative instances and features at the same time. We need to point out that not all numerical features could be appropriate to become categorical ones in the aforementioned transformation. As in our evaluation, some data from medical analysis and others are perfect to be one of such active learning applications. Along this line, we study the difference between applying the proposed feature/instance combined active learning to various data sets. A set of different vector quantization methods shall also be examined to find the best setting for effective active learning experience.