Top

Neural Computing and Applications

Published in:

23-01-2021 | Original Article

Multi-objectives TLBO hybrid method to select the related risk features with rheumatism disease

Authors: Fadhaa O. Sameer, Mohammed. J. Al-obaidi, Wasan W. Al-bassam, Ali H. Ad’hiah

Published in: Neural Computing and Applications | Issue 15/2021

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

search-config

AI-assisted search

Off

Abstract

Features subset selection was commonly used in data mining and artificial intelligence techniques to produce a model with a minimal set of features that enhances the performance of the classifier. The essential motive for selecting features is to avoid the problem of a number of dimensions trap. This paper introduces a new technique of selection of features dependent on the modified of binary teaching–learning-based optimization and the suggested method called MBTLBO. This algorithm (teaching learning-based optimization TLBO) is one of the present metaheuristic that is been widely utilized to a several of intractable optimization issues in recent times. Such algorithm has been combined with supervised data mining technique (support vector machine) for the implementation of feature subset selection problem in binary identification. The collection of specific risk features with the rheumatic disease was implemented. The findings revealed that the new approach (MBTLBO) increases the accuracy of classification.

previous article Accurate on-line support vector regression incorporated with compensated prior knowledge

next article Weakly supervised semantic segmentation by iteratively refining optimal segmentation with deep cues guidance

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

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+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

Agrawal V, Chandra S (2015) Feature selection using artificial bee colony algorithm for medical image classification, pp 2–7

Akhlaghi M, Emami F, Nozhat N (2014) Binary TLBO algorithm assisted for designing plasmonic nano bi-pyramids-based absorption coefficient. J Mod Opt 61(13):1092–1096. https://doi.org/10.1080/09500340.2014.920537CrossRef

Aletaha D, Neogi T, Silman AJ, Funovits J, Felson DT, Bingham III CO, Birnbaum NS, Burmester GR, Bykerk VP, Cohen MD, Combe B et al (2010) 2010 Rheumatoid arthritis classification criteria: an American college of rheumatology/European league against rheumatism collaborative initiative. Arthritis Rheumatism 62(9):2569–2581

Allam M, Nandhini M (2018) Optimal feature selection using binary teaching learning based optimization algorithm. J King Saud Univ Comput Inf Sci. https://doi.org/10.1016/j.jksuci.2018.12.001CrossRef

Babatunde OH, Armstrong L, Leng J, Diepeveen D (2014) A genetic algorithm-based feature selection. Int J Electron Commun Comput Eng 5(4):899–905

Cervantes J, Garcia-lamont F, Rodríguez-mazahua L, Lopez A (2019) Neurocomputing a comprehensive survey on support vector machine classification: applications, challenges and trends. Neurocomputing. https://doi.org/10.1016/j.neucom.2019.10.118CrossRef

Chen X (2015) A new clustering algorithm based on near neighbor influence. Expert systems with applications. Elsevier, Amsterdam

Covões TF, Hruschka ER, de Castro LN, Santos ÁM (2009) A Cluster-based feature selection approach. In: Corchado E, Wu X, Oja E, Herrero Á, Baruque B (eds) Hybrid artificial intelligence systems. HAIS 2009. Lecture notes in computer science, vol 5572. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02319-4_20

Deng X, Li Y, Weng J, et al. (2019) Feature selection for text classification: a review. Multimed Tools Appl 78:3797–3816. https://doi.org/10.1007/s11042-018-6083-5CrossRef

10.

Forest O-P, Rodrigues D, Pereira LAM, Nakamura RYM, Costa KAP, Yang X-S, Souza AN, Paulo J, Forest O-P (2014) Expert systems with applications a wrapper approach for feature selection based on bat algorithm. Expert Syst Appl 41(5):2250–2258. https://doi.org/10.1016/j.eswa.2013.09.023CrossRef

11.

Gunavathi C, Premalatha K (2015) Performance analysis of genetic algorithm with KNN and SVM for feature selection in tumor classification, no. June

12.

Hafez AI, Zawbaa HM, Emary E, Mahmoud HA, Hassanien AE (2015) An innovative approach for feature selection based on chicken swarm optimization. In: 2015 7th International conference of soft computing and pattern recognition (SoCPaR), Fukuoka, pp. 19–24. https://doi.org/10.1109/SOCPAR.2015.7492775

13.

Hou J, Ren Z, Lu P, Zhang K (2018) An improved teaching-learning-based optimization. Chinese control conference, CCC, vol 2018-July. https://doi.org/10.23919/ChiCC.2018.8483450

14.

Jain K, Bhadauria SS (2016) Enhanced content based image retrieval using feature selection using teacher learning based optimization. Int J Comput Sci Inf Security (IJCSIS) 14(11)

15.

Kaboli M, Akhlaghi M (2016) Binary teaching-learning-based optimization algorithm is used to investigate the superscattering plasmonic nanodisk 1. Opt Spectrosc 120(6):958–963. https://doi.org/10.1134/S0030400X16060096CrossRef

16.

Khuat TT, Le MH (2018) Binary teaching–learning-based optimization algorithm with a new update mechanism for sample subset optimization in software defect prediction. Soft Comput. https://doi.org/10.1007/s00500-018-3546-6CrossRef

17.

Kiziloz HE, Deniz A, Dokeroglu T, Cosar A (2018) US CR. Neurocomputing. https://doi.org/10.1016/j.neucom.2018.04.020CrossRef

18.

Lai Z, Mo D, Wong WK, Xu Y, Miao D, Zhang D (2018) Robust discriminant regression for feature extraction. In: IEEE transactions on cybernetics, vol. 48, no. 8, pp. 2472–2484. https://doi.org/10.1109/TCYB.2017.2740949

19.

Lewes GH (2015) Support vector machines for classification, no. January. https://doi.org/10.1007/978-1-4302-5990-9

20.

Mafarja M, Jaber I, Hammouri AI, Eleyan D (2017) Binary dragonfly algorithm for feature selection, no. November. https://doi.org/10.1109/ICTCS.2017.43

21.

Mazini M, Shirazi B, Mahdavi I (2019) Anomaly network-based intrusion detection system using a reliable hybrid artificial bee colony and AdaBoost algorithms. J King Saud Univ Comput Inf Sci 31(4):541–553. https://doi.org/10.1016/j.jksuci.2018.03.011CrossRef

22.

Mohan BSS, Shanthini KS (2014) Performance analysis of classifiers with feature selection and optimization in CBIR system for biological images. https://doi.org/10.12792/icisip2014.042

23.

Moradi P, Gholampour M (2016) A hybrid particle swarm optimization for feature subset selection by integrating a novel local search strategy. Appl Soft Comput J 43:117–130. https://doi.org/10.1016/j.asoc.2016.01.044CrossRef

24.

Satapathy SC, Naik A (2013) Modified teaching–learning-based optimization algorithm for global numerical optimization—A comparative study. Swarm Evol Comput. https://doi.org/10.1016/j.swevo.2013.12.005

25.

Panda M (2017) elephant search optimization combined with deep neural network for microarray data analysis. J King Saud Univ Comput Inf Sci. https://doi.org/10.1016/j.jksuci.2017.12.002CrossRef

26.

Rao RV, Savsani VJ, Balic J (2012) Teaching–learning-based optimization algorithm for unconstrained and constrained real-parameter optimization problems. Eng Optim 44(12):1447–1462. https://doi.org/10.1080/0305215X.2011.652103CrossRef

27.

Sameer FO, Abu Bakar MR (2017) Modified Kohonen network algorithm for selection of the initial centres of Gustafson-Kessel algorithm in credit scoring. Pertanika J Sci Technol 25(1):77–90

28.

Sameer FO, Abu Bakar MR, Zaidan AA, Zaidan BB (2017) A new algorithm of modified binary particle swarm optimization based on the Gustafson-Kessel for credit risk assessment. Neural Comput Appl 31:337–346. https://doi.org/10.1007/s00521-017-3018-4CrossRef

29.

Sayed GI, Hassanien AE, Azar AT (2019) Feature selection via a novel chaotic crow search algorithm. Neural Comput Appl 31(1):171–188CrossRef

30.

Sayed GI, Khoriba G, Haggag MH (2018) A novel chaotic salp swarm algorithm for global optimization and feature selection. Appl Intell 48(10):3462–3481CrossRef

31.

Shahbeig S, Helfroush MS, Rahideh A (2016) A fuzzy multi-objective hybrid TLBO-PSO approach to select the associated genes with breast cancer. Signal Process. https://doi.org/10.1016/j.sigpro.2016.07.035CrossRef

32.

Siddiqui MK, Menendez RM, Huang X, Hussain N (2020) A review of epileptic seizure detection using machine learning classifiers. Brain Inform. https://doi.org/10.1186/s40708-020-00105-1CrossRef

33.

Sridevi T, Murugan A (2014) A novel feature selection method for effective breast cancer diagnosis and prognosis. Int J Comput Appl 88(11)

34.

Tan KC, Teoh EJ, Yu Q, Goh KC (2009) Expert systems with applications a hybrid evolutionary algorithm for attribute selection in data mining. Expert Syst Appl 36(4):8616–8630. https://doi.org/10.1016/j.eswa.2008.10.013CrossRef

35.

Thawkar S, Ingolikar R (2018) Classification of masses in digital mammograms using biogeography-based optimization technique. J King Saud Univ Comput Inf Sci. https://doi.org/10.1016/j.jksuci.2018.01.004CrossRef

36.

Ting G, Schneeweiss S, Scranton R, Katz JN, Weinblatt ME, Young M, Avorn J, Solomon DH (2008) Development of a health care utilisation data-based index for rheumatoid arthritis severity: a preliminary study. Arthritis Res Therapy 10(4):R95

37.

Tuo S, Yong L, Deng FA, Li Y, Lin Y, Lu Q (2017) HSTLBO: a hybrid algorithm based on harmony search and teaching-learning-based optimization for complex high-dimensional optimization problems, pp 1–23

38.

Wah YB, Ibrahim N, Hamid HA, Abdul-rahman S, Fong S (2018) Feature selection methods: case of filter and wrapper approaches for maximising classification accuracy. Pertanika J Sci Technol 26(1):329–340

39.

Wells G, Becker JC, Teng J, Dougados M, Schiff M, Smolen J, Aletaha D, Van Riel PLCM (2009) Validation of the 28-joint disease activity score (DAS28) and European league against rheumatism response criteria based on C-reactive protein against disease progression in patients with rheumatoid arthritis, and comparison with the DAS28 based on erythrocyte sedimentation rate. Ann Rheum Dis 68(6):954–960CrossRef

40.

Wen J, Lai Z, Zhan Y, Cui J (2016) The L 2, 1 -norm-based unsupervised optimal feature selection with applications to action recognition. Pattern Recognit 60:515–530. https://doi.org/10.1016/j.patcog.2016.06.006CrossRefMATH

Title: Multi-objectives TLBO hybrid method to select the related risk features with rheumatism disease
Authors: Fadhaa O. Sameer
Mohammed. J. Al-obaidi
Wasan W. Al-bassam
Ali H. Ad’hiah
Publication date: 23-01-2021
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 15/2021
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-020-05665-1

Springer Professional

Abstract

Please log in to get access to your license.

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

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Other articles of this Issue 15/2021

Brain tumor classification in magnetic resonance image using hard swish-based RELU activation function-convolutional neural network

An energy efficient intelligent torque vectoring approach based on fuzzy logic controller and neural network tire forces estimator

Efficient and effective training of sparse recurrent neural networks

Optimizing a two-level closed-loop supply chain under the vendor managed inventory contract and learning: Fibonacci, GA, IWO, MFO algorithms

Harris hawks optimization: a comprehensive review of recent variants and applications

BiLSTM and dynamic fuzzy AHP-GA method for procedural game level generation

Premium Partner