---
title: "Active Learning with Multi-Label SVM Classification"
authors:
  - "Xin Li"
  - "Yuhong Guo"
doi: ""
url: "https://ijcai.org/proceedings/2013/221"
pdf: "https://ijcai.org/proceedings/2013/0221.pdf"
venue: "Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence"
year: 2013
pages: "1479-1484"
track: ""
keywords:
---

# Active Learning with Multi-Label SVM Classification

Xin Li, Yuhong Guo

*Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence*, pages 1479-1484.

## Abstract

Multi-label classification, where each instance is assigned to multiple categories, is a prevalent problem in data analysis. However, annotations of multi-label instances are typically more time-consuming or expensive to obtain than annotations of single-label instances. Though active learning has been widely studied on reducing labeling effort for single-label problems, current research on multi-label active learning remains in a preliminary state. In this paper, we first propose two novel multi-label active learning strategies, a max-margin prediction uncertainty strategy and a label cardinality inconsistency strategy, and then integrate them into an adaptive framework of multi-label active learning. Our empirical results on multiple multi-label data sets demonstrate the efficacy of the proposed active instance selection strategies and the integrated active learning approach.