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Multi-Label Active Learning with Chi-Square Statistics for Image Classification

Authors:

Chen Ye,

Jian Wu,

Victor S. Sheng,

Shiquan Zhao,

Pengpeng Zhao,

Zhiming CuiAuthors Info & Claims

ICMR '15: Proceedings of the 5th ACM on International Conference on Multimedia Retrieval

Pages 583 - 586

https://doi.org/10.1145/2671188.2749365

Published: 22 June 2015 Publication History

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Abstract

Active learning is to select the most informative examples to request their labels. Most previous studies in active learning for multi-label classification didn't pay enough attention on label correlations. This leads to a bad performance for classification. In this paper, we proposed a chi-square statistics multi-label active learning (CSMAL) algorithm, which uses chi-square statistics to accurately evaluate correlations between labels. CSMAL considers not only positive relationships but also negative ones. It uses the average correlation between a potential label and its rest unlabeled labels as the label information for each sample-label pair. CSMAL further integrates uncertainty and label information to select example-label pairs to request labels. Our empirical results demonstrate that our proposed method CSMAL outperforms the state-of-the-art active learning methods for multi-label classification. It significantly reduces the labeling workloads and improves the performance of a classifier built.

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Goswami DChakraborty S(2024)Active Batch Sampling for Multi-label Classification with Binary User Feedback2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)10.1109/WACV57701.2024.00252(2522-2531)Online publication date: 3-Jan-2024
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Wang QZhang HZhang WDai LLiang YShi H(2024)Deep active learning for multi label text classificationScientific Reports10.1038/s41598-024-79249-714:1Online publication date: 15-Nov-2024
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Çelikkanat AMalliaros FPapadopoulos AAlhajj RAgarwal NMa ZRokne JAn JCharalampos CMagdy W(2022)NodeSig: Binary Node Embeddings via Random Walk DiffusionProceedings of the 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining10.1109/ASONAM55673.2022.10068621(68-75)Online publication date: 10-Nov-2022
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Index Terms

Multi-Label Active Learning with Chi-Square Statistics for Image Classification
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees

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Published In

ICMR '15: Proceedings of the 5th ACM on International Conference on Multimedia Retrieval

June 2015

700 pages

ISBN:9781450332743

DOI:10.1145/2671188

General Chairs:
Alex Hauptmann
Carnegie Mellon University, USA
,
Chong-Wah Ngo
City University of Hong Kong, China
,
Xiangyang Xue
Fudan University, China
,
Program Chairs:
Yu-Gang Jiang
Fudan University, China
,
Cees Snoek
University of Amsterdam and Qualcomm Research Netherlands
,
Nuno Vasconcelos
University of California, San Diego, USA

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 22 June 2015

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ICMR '15: International Conference on Multimedia Retrieval

June 23 - 26, 2015

Shanghai, China

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ICMR '15 Paper Acceptance Rate 48 of 127 submissions, 38%;

Overall Acceptance Rate 254 of 830 submissions, 31%

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Wang QZhang HZhang WDai LLiang YShi H(2024)Deep active learning for multi label text classificationScientific Reports10.1038/s41598-024-79249-714:1Online publication date: 15-Nov-2024
https://doi.org/10.1038/s41598-024-79249-7
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Wu JSheng VZhang JLi HDadakova TSwisher CCui ZZhao P(2020)Multi-Label Active Learning Algorithms for Image ClassificationACM Computing Surveys10.1145/337950453:2(1-35)Online publication date: 20-Mar-2020
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Adaptive Low-Rank Multi-Label Active Learning for Image Classification

Weak Labeled Multi-Label Active Learning for Image Classification

Semi-supervised multi-label classification using incomplete label information

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