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Multi-label classification using boolean matrix decomposition

Published: 26 March 2012 Publication History

Abstract

This paper introduces a new multi-label classifier based on Boolean matrix decomposition. Boolean matrix decomposition is used to extract, from the full label matrix, latent labels representing useful Boolean combinations of the original labels. Base level models predict latent labels, which are subsequently transformed into the actual labels by Boolean matrix multiplication with the second matrix from the decomposition. The new method is tested on six publicly available datasets with varying numbers of labels. The experimental evaluation shows that the new method works particularly well on datasets with a large number of labels and strong dependencies among them.

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cover image ACM Conferences
SAC '12: Proceedings of the 27th Annual ACM Symposium on Applied Computing
March 2012
2179 pages
ISBN:9781450308571
DOI:10.1145/2245276
  • Conference Chairs:
  • Sascha Ossowski,
  • Paola Lecca
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: 26 March 2012

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Author Tags

  1. Boolean matrix decomposition
  2. associations
  3. multi-label classification

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SAC 2012
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SAC 2012: ACM Symposium on Applied Computing
March 26 - 30, 2012
Trento, Italy

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SAC '12 Paper Acceptance Rate 270 of 1,056 submissions, 26%;
Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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The 40th ACM/SIGAPP Symposium on Applied Computing
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Cited By

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  • (2023)Abstracting Instance Information and Inter-Label Relations for Sparse Multi-Label ClassificationInternational Journal of Uncertainty, Fuzziness and Knowledge-Based Systems10.1142/S021848852350004631:01(25-55)Online publication date: 27-Feb-2023
  • (2023)Robust Principal Component Analysis Techniques for Ground Scene Estimation in SAR ImageryIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing10.1109/JSTARS.2023.332473216(9697-9710)Online publication date: 2023
  • (2023)A Novel Label Selection Algorithm Based on Principal Component Analysis and Sparse Approximation Solution for Multi-label Classification*2023 IEEE 35th International Conference on Tools with Artificial Intelligence (ICTAI)10.1109/ICTAI59109.2023.00085(532-537)Online publication date: 6-Nov-2023
  • (2023)Label Selection Algorithm Based on Ant Colony Optimization and Reinforcement Learning for Multi-label ClassificationNeural Information Processing10.1007/978-981-99-8073-4_39(509-521)Online publication date: 15-Nov-2023
  • (2023)Infinite Label Selection Method for Mutil-label ClassificationNeural Information Processing10.1007/978-981-99-1639-9_30(361-372)Online publication date: 15-Apr-2023
  • (2023)A Label Embedding Method via Conditional Covariance Maximization for Multi-label ClassificationDatabase and Expert Systems Applications10.1007/978-3-031-39821-6_32(393-407)Online publication date: 16-Aug-2023
  • (2022)Regularized Matrix Factorization for Multilabel Learning With Missing LabelsIEEE Transactions on Cybernetics10.1109/TCYB.2020.301689752:5(3710-3721)Online publication date: May-2022
  • (2022)Label Selection Algorithm Based on Iteration Column Subset Selection for Multi-label ClassificationDatabase and Expert Systems Applications10.1007/978-3-031-12423-5_22(287-301)Online publication date: 29-Jul-2022
  • (2021)A Globally Optimal Label Selection Method via Genetic Algorithm for Multi-label ClassificationDatabase and Expert Systems Applications10.1007/978-3-030-86475-0_24(239-247)Online publication date: 1-Sep-2021
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