Intelligent Classification Method of Archive Data Based on Multigranular Semantics
Open Access
- 14 May 2022
- journal article
- research article
- Published by Hindawi Limited in Computational Intelligence and Neuroscience
- Vol. 2022, 1-9
- https://doi.org/10.1155/2022/7559523
Abstract
With the rapid development of information technology, the amount of data in various digital archives has exploded. How to reasonably mine and analyze archive data and improve the effect of intelligent management of newly included archives has become an urgent problem to be solved. The existing archival data classification method is manual classification oriented to management needs. This manual classification method is inefficient and ignores the inherent content information of the archives. In addition, for the discovery and utilization of archive information, it is necessary to further explore and analyze the correlation between the contents of the archive data. Facing the needs of intelligent archive management, from the perspective of the text content of archive data, further analysis of manually classified archives is carried out. Therefore, this paper proposes an intelligent classification method for archive data based on multigranular semantics. First, it constructs a semantic-label multigranular attention model; that is, the output of the stacked expanded convolutional coding module and the label graph attention module are jointly connected to the multigranular attention Mechanism network, the weighted label output by the multigranularity attention mechanism network is used as the input of the fully connected layer, and the output value of the fully connected layer used to map the predicted label is input into a Sigmoid layer to obtain the predicted probability of each label; then, the model for training: use the multilabel data set to train the constructed semantic-label multigranularity attention model, adjust the parameters until the semantic-label multigranularity attention model converges, and obtain the trained semantic-label multigranularity attention model. Taking the multilabel data set to be classified as input, the semantic-label multigranularity attention model after training outputs the classification result.Keywords
Funding Information
- Education Department of Jilin Province (JJKH20221219SK)
This publication has 24 references indexed in Scilit:
- Multi-granulation fuzzy preference relation rough set for ordinal decision systemFuzzy Sets and Systems, 2017
- Similarity-Based Approach for Group Decision Making with Multi-Granularity Linguistic InformationInternational Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2016
- Binary Classification of Multigranulation Searching Algorithm Based on Probabilistic DecisionMathematical Problems in Engineering, 2016
- Cardiac arrhythmia classification using multi-granulation rough set approachesInternational Journal of Machine Learning and Cybernetics, 2016
- Improving Supervised Learning Classification Methods Using Multigranular Linguistic Modeling and Fuzzy EntropyIEEE Transactions on Fuzzy Systems, 2016
- A Bayesian Classification Approach Using Class-Specific Features for Text CategorizationIEEE Transactions on Knowledge and Data Engineering, 2016
- An Approach to Hesitant Fuzzy Group Decision Making with Multi-Granularity Linguistic InformationInformatica, 2016
- Pessimistic multi-granulation rough set-based classification for heart valve disease diagnosisInternational Journal of Modelling, Identification and Control, 2016
- Integration of selecting and scheduling urban road construction projects as a time-dependent discrete network design problemEuropean Journal of Operational Research, 2015
- Sustainable Transportation Network Design with Stochastic Demands and Chance ConstraintsInternational Journal of Sustainable Transportation, 2014