Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Prediction of Depression Using EEG: A Comparative Study Read chapter Read first chapter

2021 | OriginalPaper | Chapter

Effectiveness of Swarm-Based Metaheuristic Algorithm in Data Classification Using Pi-Sigma Higher Order Neural Network

Authors : Nibedan Panda, Santosh Kumar Majhi

Published in: Progress in Advanced Computing and Intelligent Engineering

Publisher: Springer Singapore

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

In this paper, Salp Swarm Algorithm (SSA) is employed in training the Higher Order Neural Network (HONN) for data classification task. In machine learning approach, to train artificial neural network is considered a difficult task which gains the attention of researchers recently. The difficulty of Artificial Neural Networks (ANNs) arises due to its nonlinearity nature and unknown set of initial parameters. Traditional training algorithms exhibit poor performance in terms of local optima avoidance and convergence rate, for which metaheuristic based optimization emerges as a suitable alternative. The performance of the proposed SSA-based HONN method has been verified by considering various classification measures over benchmark datasets chosen from UCI repository and the outcome obtained by the said method is compared with the state-of-art evolutionary algorithms. From the outcome reported, the proposed method outperforms over the recent algorithms which confirm its supremacy in terms of better exploration and exploitation capability.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Machine Learning Methods for Vehicle Positioning in Vehicular Ad-Hoc Networks
next chapter Deep Learning for Cover Song Apperception
Literature
1.
Panda, N., Majhi, S.K.: Improved Salp Swarm algorithm with space transformation search for training neural network. Arab. J. Sci. Eng. 1–19
2.
Shames, D.S., Wistuba, I.I.: The evolving genomic classification of lung cancer. J. Pathol. 232(2), 121–133 (2014)
3.
Wu, Z., Zhu, H., Li, G., Cui, Z., Huang, H., Li, J., Chen, E., Xu, G.: An efficient wikipedia semantic matching approach to text document classification. Inf. Sci. 393, 15–28 (2017)MathSciNet
4.
Deng, X., Li, Y., Weng, J., Zhang, J.: Feature selection for text classification: a review. Multimed. Tools Appl. 78(3), 3797–3816 (2019)
5.
Sun, Y., Xue, B., Zhang, M., Yen, G.G.: Evolving deep convolutional neural networks for image classification. IEEE Trans. Evol. Comput. (2019)
6.
Mohapatra, P., Chakravarty, S., Dash, P.K.: An improved cuckoo search based extreme learning machine for medical data classification. Swarm Evol. Comput. 24, 25–49 (2015)
7.
Tripathy, A., Agrawal, A., Rath, S.K.: Classification of sentiment reviews using n-gram machine learning approach. Expert Syst. Appl. 57, 117–126 (2016)
8.
Jiang, Y.G., Wu, Z., Tang, J., Li, Z., Xue, X., Chang, S.F.: Modeling multimodal clues in a hybrid deep learning framework for video classification. IEEE Trans. Multimed. 20(11), 3137–3147 (2018)
9.
Zhang, J., Chen, C., Xiang, Y., Zhou, W., Xiang, Y.: Internet traffic classification by aggregating correlated naive bayes predictions. IEEE Trans. Inf. Forens. Secur. 8(1), 5–15 (2012)
10.
Panda, N., Majhi, S.K.: How effective is the salp swarm algorithm in data classification. In: Computational Intelligence in Pattern Recognition, pp. 579–588. Springer, Singapore (2020)
11.
Tang, B., Kay, S., He, H.: Toward optimal feature selection in naive Bayes for text categorization. IEEE Trans. Knowl. Data Eng. 28(9), 2508–2521 (2016)
12.
Rao, H., Shi, X., Rodrigue, A.K., Feng, J., Xia, Y., Elhoseny, M., Yuan, X., Gu, L.: Feature selection based on artificial bee colony and gradient boosting decision tree. Appl. Soft Comput. 74, 634–642 (2019)
13.
Tang, Y., Jing, L., Atkinson, P.M., Li, H.: A multiple-point spatially weighted k-NN classifier for remote sensing. Int. J. Remote Sens. 37(18), 4441–4459 (2016)
14.
Kumar, S., Singh, S., Kumar, J.: Multiple face detection using hybrid features with SVM classifier. In Data and Communication Networks, pp. 253–265. Springer, Singapore (2019)
15.
McCulloch, W.S., Pitts, W.: A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5(4), 115–133 (1943)MathSciNetMATH
16.
Panda, N., Majhi, S.K.: How effective is Spotted Hyena optimizer for training multilayer perceptrons. Int. J. Recent Technol. Eng. 4915–4927 (2019)
17.
Vardhana, M., Arunkumar, N., Lasrado, S., Abdulhay, E., Ramirez-Gonzalez, G.: Convolutional neural network for bio-medical image segmentation with hardware acceleration. Cogn. Syst. Res. 50, 10–14 (2018)
18.
Anthimopoulos, M., Christodoulidis, S., Ebner, L., Christe, A., Mougiakakou, S.: Lung pattern classification for interstitial lung diseases using a deep convolutional neural network. IEEE Trans. Med. Imaging 35(5), 1207–1216 (2016)
19.
Watkins, Y.Z., Sayeh, M.R.: Image data compression and noisy channel error correction using deep neural network. Procedia Comput. Sci. 95, 145–152 (2016)
20.
Villarrubia, G., De Paz, J.F., Chamoso, P., De la Prieta, F.: Artificial neural networks used in optimization problems. Neurocomputing 272, 10–16 (2018)
21.
Ehlers, R.: Formal verification of piece-wise linear feed-forward neural networks. In: International Symposium on Automated Technology for Verification and Analysis, pp. 269–286. Springer, Cham (October 2017)
22.
Tajbakhsh, N., Shin, J.Y., Gurudu, S.R., Hurst, R.T., Kendall, C.B., Gotway, M.B., Liang, J.: Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans. Med. Imag. 35(5), 1299–1312 (2016)
23.
Roy, K., Mandal, K.K., Mandal, A.C.: Ant-Lion Optimizer algorithm and recurrent neural network for energy management of micro grid connected system. Energy 167, 402–416 (2019)
24.
Aljarah, I., Faris, H., Mirjalili, S., Al-Madi, N.: Training radial basis function networks using biogeography-based optimizer. Neural Comput. Appl. 29(7), 529–553 (2018)
25.
Uriarte, A., Melin, P., Valdez, F.: Optimization of modular neural network architectures with an improved particle swarm optimization algorithm. In: Recent Developments and the New Direction in Soft-Computing Foundations and Applications, pp. 165–174. Springer, Cham (2018)
26.
Misra, B.B., Dehuri, S.: Functional link artificial neural network for classification task in data mining (2007)
27.
Nayak, J., Naik, B., Behera, H.S.: A novel nature inspired firefly algorithm with higher order neural network: performance analysis. Eng. Sci. Technol. Int. J. 19(1), 197–211 (2016a)
28.
Nayak, J., Naik, B., Behera, H.S.: Solving nonlinear classification problems with black hole optimisation and higher order Jordan Pi-sigma neural network: a novel approach. Int. J. Comput. Syst. Eng. 2(4), 236–251 (2016b)
29.
Mirjalili, S.: Genetic algorithm. In: Evolutionary Algorithms and Neural Networks, pp. 43–55. Springer, Cham (2019)
30.
Wu, G., Shen, X., Li, H., Chen, H., Lin, A., Suganthan, P.N.: Ensemble of differential evolution variants. Inf. Sci. 423, 172–186 (2018)MathSciNet
31.
Du, K.L., Swamy, M.N.S.: Particle swarm optimization. In: Search and Optimization by Metaheuristics, pp. 153–173. Birkhäuser, Cham (2016)
32.
Dorigo, M., Stützle, T.: Ant colony optimization: overview and recent advances. In: Handbook of Metaheuristics, pp. 311–351. Springer, Cham (2019)
33.
Karaboga, D., Gorkemli, B., Ozturk, C., Karaboga, N.: A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artif. Intell. Rev. 42(1), 21–57 (2014)
34.
Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)
35.
Yang, X.S., Hossein Gandomi, A.: Bat algorithm: a novel approach for global engineering optimization. Eng. Comput. 29(5), 464–483 (2012)
36.
Shin, Y., Ghosh, J.: The pi-sigma network: An efficient higher-order neural network for pattern classification and function approximation. In: IJCNN-91-Seattle International Joint Conference on Neural Networks, vol. 1, pp. 13–18. IEEE (July 1991)
37.
Akram, U., Ghazali, R., Mushtaq, M.F.: A comprehensive survey on Pi-Sigma neural network for time series prediction. J. Telecommun. Electron. Comput. Eng. (JTEC) 9(3–3), 57–62 (2017)
38.
Mirjalili, S., Gandomi, A.H., Mirjalili, S.Z., Saremi, S., Faris, H., Mirjalili, S.M.: Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv. Eng. Softw. 114, 163–191 (2017)
39.
Bache, K., Lichman, M.: UCI Machine Learning Repository. University of California. School of information and Computer Science, Irvine, CA, 28 (2013). https://​archive.​ics.​uci.​edu/​ml. Luo, Q., Li, J., Zhou, Y. 2019. Spotted hyena optimizer with lateral inhibition for image matching. Multimedia Tools and Applications, pp. 1–20
Metadata
Title
Effectiveness of Swarm-Based Metaheuristic Algorithm in Data Classification Using Pi-Sigma Higher Order Neural Network
Authors
Nibedan Panda
Santosh Kumar Majhi
Copyright Year
2021
Publisher
Springer Singapore
Book
Progress in Advanced Computing and Intelligent Engineering

Print ISBN: 978-981-15-6352-2

Electronic ISBN: 978-981-15-6353-9

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-981-15-6353-9_8