Optimal feature selection and invasive weed tunicate swarm algorithm-based hierarchical attention network for text classification

Figures & data

Table 1. Review on recent meta-heuristics algorithms.

Reference	Algorithm	Advantage	Disadvantage
(Azizi et al., 2023)	Fire Hawk Optimizer (FHO)	Solved real-world optimisation problems. Able to converge to the global best.	Complex constraint issues are not considered.
(Naruei & Keynia, 2022)	Wild Horse Optimizer (WHO)	Solved complex problems. Better ability to clear test functions.	It needs to implement multi and binary-objective version.
(Che & He, 2022)	Enhanced Seagull Optimization Algorithm (ESOA)	Easy implementation. Simple structure. Apply on Engineering design problems.	Unable to handle dynamic optimisation issues.
(Wang et al., 2022)	Artificial Rabbits Optimization (ARO)	Minimise the computational complexity.	Struggle for handling emerging complex optimisation issues.
(Xue & Shen, 2023)	Dung Beetle Optimizer (DBO)	High convergence rate.	Not applicable in some engineering design issues.
(Tanhaeean et al., 2022)	Boxing Match Algorithm (BMA)	Solved various difficult numerical issues.	Accuracy of the optimal solution was reduced in some mathematical functions.
(Abdollahzadeh et al., 2022)	Mountain Gazelle Optimizer (MGO)	Avoid premature convergence.	Cost was high.
(Gharehchopogh et al., 2021)		High accuracy and convergence rate.	Analysis was not done for real-world engineering issues.
(Misaghi & Yaghoobi, 2019)	Improved IWO	Fast convergence and high accuracy. Improve the population diversity.	The performance was reduced some complex functions.
(Kaur et al., 2020)	TSA	Applicable for complex unconstrained design issues.	Not applicable for multi-objective optimisation issues.

Figure 1. Schematic view of IWTSO-based HAN.

Figure 2. Solution encoding.

Algorithm 1. Pseudo code of IWTSO

Sl. No	Pseudo code of IWTSO
1	Input: $\eta (s)$, $H_l^s$
2	Output: $H_l^{s+1}$
3	Population initialisation
4	Fitness determination
5	Solution update by Eq. (19)(19) $\begin{array}{rl}& H_l^{s+1}=\frac{1+\overrightarrow{B}}{\overrightarrow{B}}{H}_l^s(\eta (s)-1)+r_{and}{H}_l^s\end{array}$(19)
6	Feasibility evaluation
7	Termination

Figure 3. Structure of HAN.

Table 2. Experimental parameters.

Parameter	Value
Maximum features	200000
Maximum sentence number	10
Maximum word number	1
Vector size of word embedding	5
Activation function	Relu
Loss function	categorical_crossentropy
Optimiser	Adam
Epochs	10
Batch size	50

Table 3. Dataset details.

Parameter	Value
Reuter dataset
Number of instances	21578
Data Set characteristics	Text
Number of attributes	5
Attribute characteristics	Categorical
Number of web hits	245593
20-Newsgroup dataset
Files	20
Total messages	18828
Real-time data
Total topics	20
Total papers	250
Total documents	5000

Figure 4. Performance analysis with Reuter dataset, (a) accuracy, (b) TPR, (c) TNR, (d) FNR, (e) precision.

Figure 5. Performance analysis with 20-Newsgroup dataset, (a) accuracy, (b) TPR, (c) TNR, (d) FNR, (e) precision.

Figure 6. Analysis with Reuter dataset, (a) accuracy, (b) TPR, (c) TNR, (d) FNR, (e) precision.

Figure 7. Analysis with 20-Newsgroup dataset, (a) accuracy, (b) TPR, (c) TNR, (d) FNR, (e) precision.

Figure 8. Analysis with real-time data, (a) accuracy, (b) TPR, (c) TNR, (d) FNR, (e) precision.

Table 4. Comparative discussion.

Metrics	ISCA	LFNN	NDM-BJO	HRFS	Improved IWO-HAN	TSA-HAN	LSTM	RNN	SVM	Proposed IWTSO-based HAN
Reuter dataset
Accuracy (%)	79.4	81.2	82.5	83.5	84.1	87.4	86.5	88.6	90.9	91.3
TNR (%)	79.4	83.2	84.5	85.4	86.5	90.1	88.7	91.6	92.6	93.2
TPR (%)	78.1	79.5	80.1	81.0	81.4	86.5	83.7	88.8	89.8	90.3
FNR	0.218	0.204	0.198	0.190	0.186	0.137	0.147	0.126	0.109	0.096
Precision (%)	75.7	77.6	81.8	83.7	89.5	91.5	90.7	93.7	94.8	96.4
20-Newsgroup dataset
Accuracy (%)	81.3	84.1	85.6	86.5	87.1	91.3	88.7	91.7	91.9	92.4
TNR (%)	82.5	85.2	86.5	87.1	88.5	91.3	89.8	92.6	93.6	94.1
TPR (%)	82.1	85.1	86.0	86.4	87.5	88.4	87.9	90.8	91.6	92.4
FNR	0.178	0.148	0.140	0.135	0.124	0.115	0.122	0.109	0.098	0.075
Precision (%)	76.9	78.5	83.5	89.8	91.4	93.5	92.7	94.5	94.9	95.4
Real-time data
Accuracy (%)	82.4	86.5	87.1	88.0	89.4	92.1	90.8	92.7	92.9	93.4
TNR (%)	83.5	86.5	85.2	85.3	84.1	89.9	87.7	90.5	92.6	93.2
TPR (%)	78.3	80.4	81.4	82.4	84.1	88.9	87.5	90.2	91.6	92.1
FNR	0.217	0.196	0.186	0.176	0.159	0.111	0.126	0.098	0.087	0.079
Precision (%)	77.6	79.4	84.1	89.4	92.5	94.7	93.6	95.3	95.8	96.4

Azizi, M., Talatahari, S., & Gandomi, A. H. (2023). Fire Hawk optimizer: A novel metaheuristic algorithm. Artificial Intelligence Review, 56(1), 287–363. doi:10.1007/s10462-022-10173-w

 Web of Science ®Google Scholar

Naruei, I., & Keynia, F. (2022). Wild horse optimizer: A new meta-heuristic algorithm for solving engineering optimization problems. Engineering with Computers, 38(S4), 3025–3056. doi:10.1007/s00366-021-01438-z

 Google Scholar

Che, Y., & He, D. (2022). An enhanced seagull optimization algorithm for solving engineering optimization problems. Applied Intelligence, 52(11), 13043–13081. doi:10.1007/s10489-021-03155-y

 Web of Science ®Google Scholar

Wang, L., Cao, Q., Zhang, Z., Mirjalili, S., & Zhao, W. (2022). Artificial rabbits optimization: A new bio-inspired meta-heuristic algorithm for solving engineering optimization problems. Engineering Applications of Artificial Intelligence, 114.

 PubMed Web of Science ®Google Scholar

Xue, J., & Shen, B. (2023). Dung beetle optimizer: A new meta-heuristic algorithm for global optimization. The Journal of Supercomputing, 79(7), 7305–7336. doi:10.1007/s11227-022-04959-6

 Google Scholar

Tanhaeean, M., Moghaddam, R. T., & Akbari, A. H. (2022). Boxing Match algorithm: a new meta-heuristic algorithm. Soft Computing, 26(24), 13277–13299. doi:10.1007/s00500-022-07518-6

 Web of Science ®Google Scholar

Abdollahzadeh, B., Gharehchopogh, F. S., Khodadadi, N., & Mirjalili, S. (2022). Mountain gazelle optimizer: A new nature-inspired metaheuristic algorithm for global optimization problems. Advances in Engineering Software, 174, 103282. doi:10.1016/j.advengsoft.2022.103282

 Web of Science ®Google Scholar

Gharehchopogh, F. S., Maleki, I., & Dizaji, Z. A. (2021). Chaotic vortex search algorithm: metaheuristic algorithm for feature selection. Evolutionary Intelligence.

 Web of Science ®Google Scholar

Misaghi, M., & Yaghoobi, M. (2019). Improved invasive weed optimization algorithm (IWO) based on chaos theory for optimal design of PID controller. Journal of Computational Design and Engineering, 6(3), 284–295. doi:10.1016/j.jcde.2019.01.001

 Web of Science ®Google Scholar

Kaur, S., Awasthi, L. K., Sangal, A. L., & Dhiman, G. (2020). Tunicate Swarm algorithm: A new bio-inspired based metaheuristic paradigm for global optimization. Engineering Applications of Artificial Intelligence, 90, 103541. doi:10.1016/j.engappai.2020.103541

 Web of Science ®Google Scholar