2020/09/06 by Bhavya Ghai, Ghai, Bhavya, Q. Vera Liao +5
Computer Science · #Artificial Intelligence (cs.AI) #Data Stream Mining Techniques #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Machine Learning (cs.LG) #Machine Learning and Algorithms #Machine Learning and Data Classification #cs.AI #cs.HC #cs.LG
paper · pdf · doi:10.48550/arxiv.2009.04568
Accepted at Workshop on Data Science with Human in the Loop (DaSH) @ ACM SIGKDD 2020
arxiv created 2020/09/06 · openalex publication_date 2020/09/06 · arxiv updated 2020/09/11 · openalex created_date 2021/04/13 · openalex updated_date 2026/07/28
We propose a new active learning (AL) framework, Active Learning++, which can utilize an annotator's labels as well as its rationale. Annotators can provide their rationale for choosing a label by ranking input features based on their importance for a given query. To incorporate this additional input, we modified the disagreement measure for a bagging-based Query by Committee (QBC) sampling strategy. Instead of weighing all committee models equally to select the next instance, we assign higher weight to the committee model with higher agreement with the annotator's ranking. Specifically, we generated a feature importance-based local explanation for each committee model. The similarity score between feature rankings provided by the annotator and the local model explanation is used to assign a weight to each corresponding committee model. This approach is applicable to any kind of ML model using model-agnostic techniques to generate local explanation such as LIME. With a simulation study, we show that our framework significantly outperforms a QBC based vanilla AL framework.