Combination of Pattern Classifiers Based on Naive Bayes and Fuzzy Integral Method for Biological Signal Applications

Author(s): Omid Akbarzadeh, Mohammad R. Khosravi*, Mehdi Shadloo-Jahromi

Journal Name: Current Signal Transduction Therapy

Volume 15 , Issue 2 , 2020


DOI : 10.2174/1574362414666190320163953

  Journal Home
Become EABM
Become Reviewer
Call for Editor

Graphical Abstract:


Abstract:

Background: Achieving the best possible classification accuracy is the main purpose of each pattern recognition scheme. An interesting area of classifier design is to design for biomedical signal and image processing.

Materials and Methods: In the current work, in order to increase recognition accuracy, a theoretical frame for combination of classifiers is developed. This method uses different pattern representations to show that a wide range of existing algorithms could be incorporated as the particular cases of compound classification where all the pattern representations are used jointly to make an accurate decision.

Results: The results show that the combination rules developed under the Naive Bayes and Fuzzy integral method outperforms other classifier combination schemes.

Conclusions: The performance of different combination schemes has been studied through an experimental comparison of different classifier combination plans. The dataset used in the article has been obtained from biological signals.

Keywords: Classification, classifier combination, majority vote, integral method, naive bayes, experimental comparison.

[1]
Lam L, Suen CY. A theoretical analysis of the application of majority voting to pattern recognition. Proceedings of the 12th IAPR International Conference on Pattern Recognition 1994; 9(13): 418-20.
[2]
Lee D-S, Srihari SN. Handprinted digit recognition: a comparison of algorithms. Pre-Proceeding of 3rd International Workshop on Frontiers in Handwriting Recognition; 1993 May 25-27; Buffalo, USA 153-62.
[3]
Noumi T, Matsui T, Yamashita I, Wakahara T, Tsu-tsumida T. Results of the Second IPTP Character Recognition Competition and studies on multi-expert handwritten numeral recognition. Proceeding of 4th lnternational Workshop on Frontiers in Handwriting Recognition. Japan. 1994; pp. 338-46.
[4]
Denisov DA. model-based chromosome recognition via hypotheses construction/verification. Pattern Recognit Lett 1994; 15: 299-307.
[http://dx.doi.org/10.1016/0167-8655(94)90063-9]
[5]
Kittler J. combining evidence in personal identity verification systems. Pattern Recognit Lett 1997; 845-52.
[http://dx.doi.org/10.1016/S0167-8655(97)00062-7]
[6]
Shapire RE, Freund Y, Bartlett P. Boosting the Margin: A new explanation for the effectiveness of voting methods. Proceeding of 14th International Conference Machine Learning San Francisco: Morgan Kaufmann 322-0.
[7]
Tumer K, Ghosh J. analysis of decision boundaries in linearly combined neural classifiers. Pattern Recognit 1996; 29: 341-84.
[http://dx.doi.org/10.1016/0031-3203(95)00085-2]
[8]
Zheng Z, Padmanabhan B. Constructing ensembles from data envelopment analysis. INFORMS J Comput 2007; 19: 486-96.
[http://dx.doi.org/10.1287/ijoc.1060.0180]
[9]
Gaber MM. Mining data streams: A review. SIGMOD Rec 2005; 34: 18-26.
[http://dx.doi.org/10.1145/1083784.1083789]
[10]
Finlay S. Multiple classifier architectures and their application to credit risk assessment. Eur J Oper Res 2011; 210: 368-78.
[http://dx.doi.org/10.1016/j.ejor.2010.09.029]
[11]
Wang G, Ma J. A hybrid ensemble approach for enterprise credit risk assessment based on support vector machine. Expert Syst Appl 2012; 39: 5325-31.
[http://dx.doi.org/10.1016/j.eswa.2011.11.003]
[12]
Wang K, Cretu G, Stolfo S. Anomalous payload-based worm detection and signature generation. Proceedings of the 8th International Conference on Recent Advances in Intrusion Detection. 227-46.
[http://dx.doi.org/10.1007/11663812_12]
[13]
Yang T. Margin-based ensemble classifier for protein fold recognition. Expert Syst Appl 2011; 38: 12348-55.
[http://dx.doi.org/10.1016/j.eswa.2011.04.014]
[14]
Lladós J, Martí E, Villanueva JJ. Symbol recognition by errortolerant subgraph matching between region adjacency graphs. IEEE Trans Pattern Anal Mach Intell 2001; 23: 1137-43.
[http://dx.doi.org/10.1109/34.954603]
[15]
Nielsen JD, Rumí R, Salmerón A. Supervised classification using probabilistic decision graphs. Comput Stat Data Anal 2009; 53: 1299-311.
[http://dx.doi.org/10.1016/j.csda.2008.11.003]
[16]
Bishop CM. Pattern Recognition and Machine Learning. Berlin: Springer 2006.
[17]
Zhang GP. Neural networks for classification: A survey. IEEE Trans Syst Man Cybern C 2000; 30: 451-62.
[http://dx.doi.org/10.1109/5326.897072]
[18]
Wendling L, Rendek J. Symbol recognition using a 2-class hierarchical model of Choquet integrals. Proceedings of the 9th International Conference on Document Analysis and Recognition. 2007 Sept 23-26; Parana, Brazil. 634-8.
[http://dx.doi.org/10.1109/ICDAR.2007.4376992]
[19]
Terrades OR, Valveny E, Tabbone S. Optimal classifier fusion in a non-bayesian probabilistic framework. IEEE Trans Pattern Anal Mach Intell 2009; 31(9): 1630-44.
[http://dx.doi.org/10.1109/TPAMI.2008.224] [PMID: 19574623]
[20]
Jahromi MS. Feature extraction in fractional fourier domain for classification of passive sonar signals. J Signal Process Syst Signal Image Video Technol 2018; 90: 1-10.
[http://dx.doi.org/10.1007/s11265-018-1347-x]
[21]
Tavallali P, Yazdi M, Khosravi MR. Robust cascaded skin detector based on AdaBoost. Multimedia Tools Appl 2018; 78: 2599-620.
[http://dx.doi.org/10.1007/s11042-018-6385-7]
[22]
Liu Z. Combination of classifiers with optimal weight based on evidential reasoning. IEEE Trans Fuzzy Syst 2017; 26: 1217-30.
[http://dx.doi.org/10.1109/TFUZZ.2017.2718483]
[23]
Karimi V, Norouzi Y. Target detection enhancement based on waveform design in cognitive radar electronics New Zealand Conference (ENZCon) 2013; 40-5.
[24]
Karimi V, Mohseni R. Radar waveform design based on OFDM signals for cognitive radar application. International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA’18). USA. 168-9.
[25]
Karimi V, Mohseni R, Norouzi Y, Dehghani MJ. Waveform Design for cognitive radar with deterministic extended targets in the presence of clutter. Int J Commun. Network and System Sciences 2016; 9(6): 250.
[http://dx.doi.org/10.4236/ijcns.2016.96023]
[26]
Karimi V, Mohseni R, Samadi S. OFDM waveform design based on mutual information for cognitive radar applications. J Supercomput 2018.
[27]
Tavallali P, Yazdi M. Robust skin detector based on AdaBoost and statistical luminance features International Congress onTechnology, communication and Knowledge (ICTCK) 2015 Dec; Mashhad, Iran 2015.
[28]
Tavallali P, Yazdi M, Khosravi MR. An efficient training procedure for viola-jones face detector International Conference on Computational Science and Computational Intelligence (ICCSCI) 2017 Dec; Las Vegas, USA 2017
[http://dx.doi.org/10.1109/CSCI. 2017.143]
[29]
Tavallali P, Singhal M. Optimization of hierarchical regression model with application to optimizing multi-response regression kary trees Association for the Advancement of Artificial Intelligence (AAAI); 2019 Jan; Honolulu, Hawaii USA 2019.
[30]
Carreira-Perpinan M, Tavallali P. Alternating optimization of decision trees, with application to learning sparse oblique trees Advances in Neural Information Processing Systems (NeurIPS); 2018 Dec; Montreal, Canada 2018.
[31]
Gao C. Privacy-preserving Naive Bayes classifiers secure against the substitution-then-comparison attack. Inf Sci 2018; 444: 72-88.
[http://dx.doi.org/10.1016/j.ins.2018.02.058]
[32]
Yu Z, Lu Y, Zhang J, et al. Progressive semisupervised learning of multiple classifiers. IEEE Trans Cybern 2018; 48(2): 689-702.
[http://dx.doi.org/10.1109/TCYB.2017.2651114] [PMID: 28113355]


open access plus

Rights & PermissionsPrintExport Cite as

Article Details

VOLUME: 15
ISSUE: 2
Year: 2020
Published on: 01 December, 2020
Page: [136 - 143]
Pages: 8
DOI: 10.2174/1574362414666190320163953

Article Metrics

PDF: 19
HTML: 3
PRC: 2



Most Downloaded Article(s)