research-article

Research on Multi-granularity Ensemble Learning Based on Korean

Authors:

Rongyi CuiAuthors Info & Claims

CONF-CDS 2021: The 2nd International Conference on Computing and Data Science

Article No.: 47, Pages 1 - 6

https://doi.org/10.1145/3448734.3450777

Published: 17 May 2021 Publication History

Abstract

Ensemble learning can train and combine multiple classifiers where the predictions are used as new features to train a meta-classifier. This improves the accuracy of the model. This paper proposes a multi granularity model based on Stacking ensemble learning for Korean text classification. Firstly, eojeol and subeojeol granularity is proposed according to the Korean language composition. Since different feature granularity contains different semantic information, compare the six different granularities of the phoneme, syllable, subword, word, subeojeol, and eojeol in Korean text classification task. Secondly, construct suffix words based on Korean grammatical morphology and compare the different granularities effects after suffix preprocessing. Finally, propose a multi granularity ensemble learning model based on Korean called MGEL-K. To enrich the diversity of ensemble learning using different granularities, making differences between learners. The results show that MGEL-K model proposed in this paper works best in the Korean text classification task with an accuracy of 92.33%.

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Cited By

James CWood RDenholm R(2023)A multi-granular stacked regression for forecasting long-term demand in Emergency DepartmentsBMC Medical Informatics and Decision Making10.1186/s12911-023-02109-323:1Online publication date: 7-Feb-2023
https://doi.org/10.1186/s12911-023-02109-3
Jiang KZhao YCui R(2022)DIFM: An Effective Deep Interaction and Fusion Model for Sentence Matching Chinese Computational Linguistics10.1007/978-3-031-18315-7_2(19-30)Online publication date: 14-Oct-2022
https://dl.acm.org/doi/10.1007/978-3-031-18315-7_2

Index Terms

Research on Multi-granularity Ensemble Learning Based on Korean
1. Applied computing
2. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees

Index terms have been assigned to the content through auto-classification.

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CONF-CDS 2021: The 2nd International Conference on Computing and Data Science

January 2021

1142 pages

ISBN:9781450389570

DOI:10.1145/3448734

Copyright © 2021 ACM.

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Published: 17 May 2021

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CONF-CDS 2021

CONF-CDS 2021: The 2nd International Conference on Computing and Data Science

January 28 - 30, 2021

CA, Stanford, USA

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Cited By

James CWood RDenholm R(2023)A multi-granular stacked regression for forecasting long-term demand in Emergency DepartmentsBMC Medical Informatics and Decision Making10.1186/s12911-023-02109-323:1Online publication date: 7-Feb-2023
https://doi.org/10.1186/s12911-023-02109-3
Jiang KZhao YCui R(2022)DIFM: An Effective Deep Interaction and Fusion Model for Sentence Matching Chinese Computational Linguistics10.1007/978-3-031-18315-7_2(19-30)Online publication date: 14-Oct-2022
https://dl.acm.org/doi/10.1007/978-3-031-18315-7_2

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