.png){kind=icon}
返回
Active instance selection via parametric equation and instance overlap aware scheme
.png){kind=icon}
PRE
AI
DOI：10.1007/s10489-021-02395-2.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Active learning (AL) reduces the human labeling effort by learning a classifier with the small labeled data. This small labeled data is formed by those instances, which reduce generalization error the most. As AL reduces the labeling cost, but most of the pool based query strategies evaluate all unlabeled instances in each iteration of query instance selection, which makes them computationally extensive. Also, many times the query strategies lead to the selection of redundant/overlapped instances, which leads to no improvement in the generalization performance. In this work, an advanced query strategy under pool based scenario has been proposed. This query strategy utilizes parametric equation based query synthesis as the informative criterion and the instance overlap aware scheme as the representative criterion. This informative criterion identifies the input instances near the decision boundary, which speeds up the instance selection process and hence reduces the response time. On the set of identified instances, the representative criterion is used to avoid the overlapped instance selection, which improves the generalization performance. Also, a meta-learning based approach has been used to identify the values of the main parameters used in the formulated criteria. The comparison of the proposed approach with the existing baseline solutions on artificial as well as real-world datasets demonstrates that the proposed approach achieves a significant reduction in the query instance selection time, along with an enhancement in the generalization performance over the existing approaches.
Keyword:
Active learning
Active learning query strategies
Pool and query synthesis based instance selection
Efficient active learning query strategies
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.5
论文数:
7.6K
被引数:
1.7W
机构
引用论文
Visual deficits and cognitive assessment of multiple sclerosis: confounder, correlate, or both?多发性硬化症的视觉缺陷和认知评估: 混杂，相关，或两者兼而有之？
PREAI
分享收藏Active learning using rough fuzzy classifier for cancer prediction from microarray gene expression data基于粗糙模糊分类器的主动学习微阵列基因表达数据的癌症预测
PREAI
分享收藏A set of measures designed to identify overlapped instances in software defect prediction.png)
COMPUTING
COMPUTINGIF2.8
PREAI
分享收藏
查看更多内容