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Feature selection based on long short term memory for text classification

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Abstract

The selection of discriminative terms from large quantity of terms in text documents is helpful for achieving better accuracy of text classification. To focus on the task of selecting discriminative terms from text, a deep learning based feature selection method is proposed. The method is developed by using the long short term memory (LSTM) network. A deep network based on LSTM is trained in unsupervised manner to extracted deep features from bag-of-words term frequency vectors. The deep features are integrated with term frequencies to evaluate the effectiveness of terms. The proposed method extends the limitation of term frequency information by applying deep features for feature selection. Experiments in nine public datasets demonstrate better performance of our method in selecting discriminative terms than comparative methods.

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Data availability

The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This research was supported by the Fundamental Research Funds for Guangdong Natural Science Foundation, Grant No. 2022A1515011848; Guangzhou Philosophy and Social Science, Grant No. 2020GZYB04; Guangdong Philosophy and Social Science, Grant No. GD22YYJ15.

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  1. Department of Electronic Business, South China University of Technology, Guangzhou, China

    Ming Hong & Heyong Wang

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Appendix

Appendix

1.1 Results of classification accuracy analysis

Table 3

Table 3 Classification accuracy of CF dataset (Corresponding to Fig. 9)
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Table 4

Table 4 Classification accuracy of CR dataset (Corresponding to Fig. 10)
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Table 5

Table 5 Classification accuracy of CNAE dataset (Corresponding to Fig. 11)
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Table 6

Table 6 Classification accuracy of IMDB dataset (Corresponding to Fig. 12)
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Table 7

Table 7 Classification accuracy of KDC dataset (Corresponding to Fig. 13)
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Table 8

Table 8 Classification accuracy of TTC dataset (Corresponding to Fig. 14)
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Table 9

Table 9 Classification accuracy of WEBKB dataset (Corresponding to Fig. 15)
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Table 10

Table 10 Classification accuracy of R8 dataset (Corresponding to Fig. 16)
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Table 11

Table 11 Classification accuracy of R52 dataset (Corresponding to Fig. 17)
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1.2 Results of semantics analysis

The bold terms are the manually selected terms which are related to topics of datasets (all the terms are stemmed, and all the uppercases are transformed to lowercases).

Table 12

Table 12 Top 20 Terms for CF dataset (Corresponding to Fig. 18 (a))
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Table 13

Table 13 Top 20 Terms for CR dataset (Corresponding to Fig. 18 (b))
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Table 14

Table 14 Top 20 Terms for IMDB dataset (Corresponding to Fig. 18 (c))
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Table 15

Table 15 Top 20 Terms for WEBKB dataset (Corresponding to Fig. 18 (d))
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Table 16

Table 16 Top 20 Terms for R8 dataset (Corresponding to Fig. 18 (e))
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Table 17

Table 17 Top 20 Terms for R52 dataset (Corresponding to Fig. 18 (f))
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1.3 Results of sparsity analysis

Table 18

Table 18 Sparsity for CF dataset (Corresponding to Fig. 19 (a))
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Table 19

Table 19 Sparsity for CR dataset (Corresponding to Fig. 19 (b))
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Table 20

Table 20 Sparsity for CNAE dataset (Corresponding to Fig. 19 (c))
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Table 21

Table 21 Sparsity for IMDB dataset (Corresponding to Fig. 19 (d))
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Table 22

Table 22 Sparsity for KDC dataset (Corresponding to Fig. 19 (e))
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Table 23

Table 23 Sparsity for TTC dataset (Corresponding to Fig. 19 (f))
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Table 24

Table 24 Sparsity for WEBKB dataset (Corresponding to Fig. 19 (g))
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Table 25

Table 25 Sparsity for R8 dataset (Corresponding to Fig. 19 (h))
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Table 26

Table 26 Sparsity for R52 dataset (Corresponding to Fig. 19 (i))
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Hong, M., Wang, H. Feature selection based on long short term memory for text classification. Multimed Tools Appl 83, 44333–44378 (2024). https://doi.org/10.1007/s11042-023-16990-7

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