Skip to main content
SpringerLink
Log in

\(\beta\)-Hill climbing algorithm with probabilistic neural network for classification problems

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

Classification is a crucial step in the data mining field. The probabilistic neural network (PNN) is an efficient method developed for classification problems. The success factor of using PNN for classification problems implies in finding the proper weight during classification process. The main goal of this paper is to improve the performance of PNN by finding the best weight for the PNN using the recent local search approach called \(\beta\)-hill-climbing (\(\beta\)-HC) optimizer. This algorithm is an extension version of the traditional hill climbing algorithm in that it uses a stochastic operator to avoid local optima. The proposed approach is evaluated against 11 benchmark datasets ,and the experimental results showed that the proposed \(\beta\)-HC with PNN approach performed better in terms of classification accuracy than the original PNN, HC-PNN and other six well-established approaches using the same experimented benchmarks.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Abed-alguni BH, Alkhateeb F (2018) Intelligent hybrid cuckoo search and \(\beta\)-hill climbing algorithm. J King Saud Univ Comput Inf Sci. https://doi.org/10.1016/j.jksuci.2018.05.003

  • Abualigah LM, Khader AT, Al-Betar MA, Alyasseri ZAA, Alomari OA, Hanandeh ES (2017) Feature selection with \(\beta\)-hill climbing search for text clustering application. In: Information and communication technology (PICICT), 2017 Palestinian international conference on, IEEE, pp 22–27

  • Adankon MM, Cheriet M (2009) Support vector machine. In: Li SZ, Jain AK (eds) Encyclopedia of biometrics, pp 1303–1308

  • Al-Betar MA (2017) \(\beta\)-Hill climbing: an exploratory local search. Neural Comput Appl 28(1):153–168. https://doi.org/10.1007/s00521-016-2328-2

    Google Scholar 

  • Al-Betar MA, Awadallah MA, Bolaji AL, Alijla BO (2017) \(\beta\)-hill climbing algorithm for sudoku game. In: Information and communication technology (PICICT), 2017 Palestinian international conference on, IEEE, pp 84–88

  • Al-Betar MA, Awadallah MA, Doush IA, Alsukhni E, ALkhraisat H (2018) A non-convex economic dispatch problem with valve loading effect using a new modified \(\beta\)-hill climbing local search algorithm. Arab J Sci Eng 43(12):7439–7456

  • Al-Betar MA, Aljarah I, Awadallah MA, Faris H, Mirjalili S (2019) Adaptive \(\beta\)-hill climbing for optimization. Soft Comput. https://doi.org/10.1007/s00500-019-03887-7

  • Al Nsour H, Alweshah M, Hammouri AI, Al Ofeishat H, Mirjalili S (2019) A hybrid grey wolf optimiser algorithm for solving time series classification problems. J Intell Syst. https://doi.org/10.1515/jisys-2018-0129

  • Aljarah I, Faris H, Mirjalili S, Al-Madi N (2018) Training radial basis function networks using biogeography-based optimizer. Neural Comput Appl 29(7):529–553

    Article  Google Scholar 

  • Alomari OA, Khader AT, Al-Betar MA, Awadallah MA (2018) A novel gene selection method using modified mrmr and hybrid bat-inspired algorithm with \(\beta\)-hill climbing. Appl Intell 48(11):4429–4447

    Google Scholar 

  • Alshareef A, Ahmida S, Bakar AA, Hamdan AR, Alweshah M (2015a) Mining survey data on university students to determine trends in the selection of majors. In: 2015 Science and information conference (SAI), IEEE, pp 586–590

  • Alshareef AM, Bakar AA, Hamdan AR, Abdullah SMS, Alweshah M (2015b) A case-based reasoning approach for pattern detection in malaysia rainfall data. Int J Big Data Intell 2(4):285–302

    Article  Google Scholar 

  • Alsukni E, Arabeyyat OS, Awadallah MA, Alsamarraie L, Abu-Doush I, Al-Betar MA (2017) Multiple-reservoir scheduling using \(\beta\)-hill climbing algorithm. J Intell Syst 28(4):559–570. https://doi.org/10.1515/jisys-2017-0159

    Google Scholar 

  • Alweshah M (2014) Firefly algorithm with artificial neural network for time series problems. Res J Appl Sci Eng Technol 7(19):3978–3982

    Article  Google Scholar 

  • Alweshah M (2018) Construction biogeography-based optimization algorithm for solving classification problems. Neural Comput Appl 29(4):1–10

    Google Scholar 

  • Alweshah M, Abdullah S (2015) Hybridizing firefly algorithms with a probabilistic neural network for solving classification problems. Appl Soft Comput 35:513–524

    Article  Google Scholar 

  • Alweshah M, Hammouri AI, Tedmori S (2017a) Biogeography-based optimisation for data classification problems. Int J Data Min Model Manag 9(2):142–162

    Google Scholar 

  • Alweshah M, Rashaideh H, Hammouri AI, Tayyeb H, Ababneh M (2017b) Solving time series classification problems using support vector machine and neural network. Int J Data Anal Tech Strat 9(3):237–247

    Article  Google Scholar 

  • Alyasseri ZAA, Khader AT, Al-Betar MA (2017a) Optimal electroencephalogram signals denoising using hybrid \(\beta\)-hill climbing algorithm and wavelet transform. In: Proceedings of the international conference on imaging, signal processing and communication, ACM, pp 106–112

  • Alyasseri ZAA, Khader AT, Al-Betar MA, Abualigah LM (2017b) ECG signal denoising using \(\beta\)-hill climbing algorithm and wavelet transform. In: 2017 8th International conference on information technology (ICIT), IEEE, pp 96–101

  • Alyasseri ZAA, Khader AT, Al-Betar MA, Awadallah MA (2018) Hybridizing \(\beta\)-hill climbing with wavelet transform for denoising ECG signals. Inf Sci 429:229–246

    MathSciNet  Google Scholar 

  • Alzaidi AA, Ahmad M, Doja M, Al Solami E, Beg MS (2018) A new 1D Chaotic map and \(\beta\)-hill climbing for generating substitution-boxes. IEEE Access 6:55405–55418

    Google Scholar 

  • Anagaw A, Chang YL (2019) A new complement naïve Bayesian approach for biomedical data classification. J Ambient Intell Human Comput 10(10):3889–3897

    Article  Google Scholar 

  • Araghi LF, Khaloozade H, Arvan MR (2009) Ship identification using probabilistic neural networks (PNN). Proc Int Multiconf Eng Comput Sci 2:18–20

    Google Scholar 

  • Berrar DP, Downes CS, Dubitzky W (2002) Multiclass cancer classification using gene expression profiling and probabilistic neural networks. In: Biocomputing 2003, World Scientific, pp 5–16

  • Blum C, Roli A (2003) Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Comput Surv 35(3):268–308

    Article  Google Scholar 

  • Boveiri HR, Khayami R, Elhoseny M, Gunasekaran M (2019) An efficient swarm-intelligence approach for task scheduling in cloud-based internet of things applications. J Ambient Intell Human Comput 10(9):3469–3479

    Article  Google Scholar 

  • Brownlee J (2016) Supervised and unsupervised machine learning algorithms. Mach Learn Mastery 16(03)

  • Cpałka K, Rebrova O, Nowicki R, Rutkowski L (2013) On design of flexible neuro-fuzzy systems for nonlinear modelling. Int J Gen Syst 42(6):706–720

    Article  MATH  Google Scholar 

  • Craddock R, Warwick K (1996) Multi-layer radial basis function networks. an extension to the radial basis function. In: Proceedings of international conference on neural networks (ICNN’96), vol 2. IEEE, pp 700–705

  • Davis WL, Warren L (2006) Enhancing pattern classification with relational fuzzy neural networks and square BK-products. Florida State University, Tallahassee

    Google Scholar 

  • Dorigo M, Stützle T (2010) Ant colony optimization: overview and recent advances. Springer, Boston, pp 227–263

    Google Scholar 

  • El-Bouri A (2012) An investigation of initial solutions on the performance of an iterated local search algorithm for the permutation flowshop. In: 2012 IEEE Congress on evolutionary computation, IEEE, pp 1–5

  • Faris H, Aljarah I, Mirjalili S (2016) Training feedforward neural networks using multi-verse optimizer for binary classification problems. Appl Intell 45(2):322–332

    Article  Google Scholar 

  • Friedman N, Geiger D, Goldszmidt M (1997) Bayesian network classifiers. Mach Learn 29(2–3):131–163

    Article  MATH  Google Scholar 

  • Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68

    Article  Google Scholar 

  • Gorunescu F (2011) Data mining: concepts, models and techniques, vol 12. Springer Science & Business Media, Berlin

    Book  MATH  Google Scholar 

  • Hu L, Qin L, Mao K, Chen W, Fu X (2016) Optimization of neural network by genetic algorithm for flowrate determination in multipath ultrasonic gas flowmeter. IEEE Sens J 16(5):1158–1167

    Article  Google Scholar 

  • Iliou T, Anagnostopoulos CN (2010) SVM-MLP-PNN classifiers on speech emotion recognition field—a comparative study. In: 2010 Fifth international conference on digital telecommunications, IEEE, pp 1–6

  • Juang BH, Hou W, Lee CH (1997) Minimum classification error rate methods for speech recognition. IEEE Trans Speech Audio process 5(3):257–265

    Article  Google Scholar 

  • Khan A, Shah R, Imran M, Khan A, Bangash JI, Shah K (2019) An alternative approach to neural network training based on hybrid bio meta-heuristic algorithm. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-019-01373-4

  • Kwigizile V, Selekwa MF, Mussa RN (2004) Highway vehicle classification by probabilistic neural networks. In: FLAIRS conference, pp 664–669

  • LeCun Y, Boser BE, Denker JS, Henderson D, Howard RE, Hubbard WE, Jackel LD (1990) Handwritten digit recognition with a back-propagation network. In: Advances in neural information processing systems, pp 396–404

  • Manimala K, Selvi K (2007) Power quality disturbances classification using probabilistic neural network. In: International conference on computational intelligence and multimedia applications (ICCIMA 2007), vol 1. IEEE, pp 207–211

  • Mao KZ, Tan KC, Ser W (2000) Probabilistic neural-network structure determination for pattern classification. IEEE Trans Neural Netw 11(4):1009–1016

    Article  Google Scholar 

  • Mika S, Ratsch G, Weston J, Scholkopf B, Mullers KR (1999) Fisher discriminant analysis with kernels. In: Neural networks for signal processing IX: proceedings of the 1999 IEEE signal processing society workshop (Cat. No. 98TH8468), Ieee, pp 41–48

  • Ren K, Qu J (2014) Identification of shaft centerline orbit for wind power units based on hopfield neural network improved by simulated annealing. Mathematical problems in engineering 2014

  • Specht DF (1990) Probabilistic neural networks. Neural Netw 3(1):109–118

    Article  Google Scholar 

  • Specht DF (1991) A general regression neural network. IEEE Trans Neural Netw 2(6):568–576

    Article  Google Scholar 

  • Sweeney WP Jr, Musavi MT, Guidi JN (1994) Classification of chromosomes using a probabilistic neural network. Cytom J Int Soc Anal Cytol 16(1):17–24

    Google Scholar 

  • Tairan N, Zhang Q (2010) Population-based guided local search: some preliminary experimental results. In: IEEE congress on evolutionary computation, IEEE, pp 1–5

  • Wasserman P (1993) Advanced methods in neural networks

  • Xie X, Li Y, Zhou H, Zheng Y (2012) Variable neighborhood search based multi-objective dynamic crane scheduling. In: Proceedings of 2012 international conference on measurement, information and control, IEEE, vol 1, pp 457–460

  • Yampolskiy RV, Govindaraju V (2008) Behavioural biometrics: a survey and classification. Int J Biometr 1(1):81–113

    Article  Google Scholar 

  • Yan C, Xie H, Chen J, Zha Z, Hao X, Zhang Y, Dai Q (2018) A fast Uyghur text detector for complex background images. IEEE Trans Multimedia 20(12):3389–3398

    Article  Google Scholar 

  • Yan C, Li L, Zhang C, Liu B, Zhang Y, Dai Q (2019a) Cross-modality bridging and knowledge transferring for image understanding. IEEE Trans Multimedia

  • Yan C, Tu Y, Wang X, Zhang Y, Hao X, Zhang Y, Dai Q (2019b) STAT: spatial-temporal attention mechanism for video captioning. IEEE Trans Multimedia

  • Zarndt F (1995) A comprehensive case study: an examination of machine learning and connectionist algorithms. PhD thesis, Brigham Young University. Department of Computer Science

  • Zhu G, Kwong S (2010) Gbest-guided artificial bee colony algorithm for numerical function optimization. Appl Math Comput 217(7):3166–3173

    MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Prince Abdullah Bin Ghazi Faculty of Information and Communication Technology, Al-Balqa Applied University, Al-Salt, Jordan

    Mohammed Alweshah, Aram Al-Daradkeh & Saleh Oqeili

  2. Department of Information Technology, Al-Huson University College, Al-Balqa Applied University, Irbid, Jordan

    Mohammed Azmi Al-Betar & Ammar Almomani

Authors
  1. Mohammed Alweshah
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aram Al-Daradkeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohammed Azmi Al-Betar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ammar Almomani
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Saleh Oqeili
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammed Alweshah.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Alweshah, M., Al-Daradkeh, A., Al-Betar, M.A. et al. \(\beta\)-Hill climbing algorithm with probabilistic neural network for classification problems. J Ambient Intell Human Comput 11, 3405–3416 (2020). https://doi.org/10.1007/s12652-019-01543-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-019-01543-4

Keywords

Search

Navigation