Evolving Long Short-Term Memory Network-Based Text Classification. (21st February 2022)

• Record Type: Journal Article
• Authors: Singh, Arjun; Dargar, Shashi Kant; Gupta, Amit; Kumar, Ashish; Srivastava, Atul Kumar; Srivastava, Mitali; Kumar Tiwari, Pradeep; Ullah, Mohammad Aman
• Other Names: Koundal Deepika Academic Editor.
• Is Part Of: Computational intelligence and neuroscience. Volume 2022(2022)
• Publication Date: 2022-02-21
• Subjects: Neurosciences -- Data processing -- Periodicals; Computational intelligence -- Periodicals; Computational neuroscience -- Periodicals; 612.80285
• DOI: 10.1155/2022/4725639
• Languages: English
• ISSNs: 1687-5265

Abstract

Abstract : Recently, long short-term memory (LSTM) networks are extensively utilized for text classification. Compared to feed-forward neural networks, it has feedback connections, and thus, it has the ability to learn long-term dependencies. However, the LSTM networks suffer from the parameter tuning problem. Generally, initial and control parameters of LSTM are selected on a trial and error basis. Therefore, in this paper, an evolving LSTM (ELSTM) network is proposed. A multiobjective genetic algorithm (MOGA) is used to optimize the architecture and weights of LSTM. The proposed model is tested on a well-known factory reports dataset. Extensive analyses are performed to evaluate the performance of the proposed ELSTM network. From the comparative analysis, it is found that the LSTM network outperforms the competitive models.