A New Hybrid Based on Long Short-Term Memory Network with Spotted Hyena Optimization Algorithm for Multi-Label Text Classification

Hamed Khataei Maragheh, Farhad Soleimanian Gharehchopogh, Kambiz Majidzadeh and Amin Babazadeh Sangar
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Hamed Khataei Maragheh: Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia 5756151818, Iran
Farhad Soleimanian Gharehchopogh: Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia 5756151818, Iran
Kambiz Majidzadeh: Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia 5756151818, Iran
Amin Babazadeh Sangar: Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia 5756151818, Iran

Mathematics, 2022, vol. 10, issue 3, 1-24

Abstract: An essential work in natural language processing is the Multi-Label Text Classification (MLTC). The purpose of the MLTC is to assign multiple labels to each document. Traditional text classification methods, such as machine learning usually involve data scattering and failure to discover relationships between data. With the development of deep learning algorithms, many authors have used deep learning in MLTC. In this paper, a novel model called Spotted Hyena Optimizer (SHO)-Long Short-Term Memory (SHO-LSTM) for MLTC based on LSTM network and SHO algorithm is proposed. In the LSTM network, the Skip-gram method is used to embed words into the vector space. The new model uses the SHO algorithm to optimize the initial weight of the LSTM network. Adjusting the weight matrix in LSTM is a major challenge. If the weight of the neurons to be accurate, then the accuracy of the output will be higher. The SHO algorithm is a population-based meta-heuristic algorithm that works based on the mass hunting behavior of spotted hyenas. In this algorithm, each solution of the problem is coded as a hyena. Then the hyenas are approached to the optimal answer by following the hyena of the leader. Four datasets are used (RCV1-v2, EUR-Lex, Reuters-21578, and Bookmarks) to evaluate the proposed model. The assessments demonstrate that the proposed model has a higher accuracy rate than LSTM, Genetic Algorithm-LSTM (GA-LSTM), Particle Swarm Optimization-LSTM (PSO-LSTM), Artificial Bee Colony-LSTM (ABC-LSTM), Harmony Algorithm Search-LSTM (HAS-LSTM), and Differential Evolution-LSTM (DE-LSTM). The improvement of SHO-LSTM model accuracy for four datasets compared to LSTM is 7.52%, 7.12%, 1.92%, and 4.90%, respectively.

Keywords: multi-label text classification; deep learning neural networks; short-term long-term memory; spotted hyena optimizer (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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