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Neural Processing Letters
Erschienen in: Neural Processing Letters 3/2017

27.01.2017

Learners Reliability Estimated Through Neural Networks Applied to Build a Novel Hybrid Ensemble Method

verfasst von: Marco Vannucci, Valentina Colla, Silvia Cateni

Erschienen in: Neural Processing Letters | Ausgabe 3/2017

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Abstract

In this paper a novel hybrid ensemble method aiming at the improvement of models accuracy in regression tasks is presented. The basic idea of the approach is the creation of an ensemble learner composed by a strong learner which is trained by exploiting data belonging to the whole training dataset and a set of specialised weak learners trained by using data coming from limited regions of the input space determined by means of a self organising map based clustering. In this context, different methods have been tested for the design of the learners, including a hierarchical approach. In the simulation phase, the strong and weak learners operate according to their punctual self-estimated reliabilities so as to exploit their strengths and overcome their weaknesses. The method has been tested on literature and real world datasets achieving competitive results by outperforming other ensemble methods on most of the tested datasets and reducing the average absolute error by up to 10%.
Vorheriger Artikel Identifying Market Behaviours Using European Stock Index Time Series by a Hybrid Segmentation Algorithm
Nächster Artikel Interpreting Decision Support from Multiple Classifiers for Predicting Length of Stay in Patients with Colorectal Carcinoma

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Literatur
1.
Dietterich T (2000) Ensemble methods in machine learning. Multiple classifier systems, lecture notes in computer science. Springer, Berlin
2.
Embrechts MJ, Gatti CJ, Linton J, Gruber T, Sick B (2012) Forecasting exchange rates with ensemble neural networks and ensemble K-PLS: a case study for the US Dollar per Indian Rupee. In: The 2012 international joint conference on neural networks (IJCNN), pp 1–8
3.
Cheng C, Xu W, Wang J (2012) A comparison of ensemble methods in financial market prediction. In: 2012 Fifth international joint conference on computational sciences and optimization (CSO), pp 755–759
4.
Hirose H, Zaman F (2011) More accurate diagnosis in electric power apparatus conditions using ensemble classification methods. IEEE Trans Dielectr Electr Insul 18(5):1584–1590CrossRef
5.
Jiang Y, Zhou Z-H (2004) SOM ensemble-based image segmentation. Neural Process Lett 20(3):171–178CrossRef
6.
Baños O, Galvez J-M, Damas M, GuillÃn A, Herrera L-J, Pomares H, Rojas I, Villalonga C, Hong CS, Lee S (2015) Multiwindow fusion for wearable activity recognition. Lecture Notes Comput Sci 9095:290–297CrossRef
7.
Mu X, Watta P, Hassoun MH (2009) Analysis of a plurality voting-based combination of classifiers. Neural Process Lett 29(2):89–107CrossRef
8.
Hashemi HB, Yazdani N, Shakery A, Naeini MP (2010) Application of ensemble models in web ranking. In: 2010 5th international symposium on telecommunications (IST), pp 726–731
9.
Hansen LK, Salamon P (1990) Neural network ensembles. IEEE Trans Pattern Anal Mach Intell 12(10):993–1001CrossRef
10.
Opitz D, Maclin R (1999) Popular ensemble methods: an empirical study. J Artif Intell Res 11:169–198MATH
11.
Wei W, Yaoyao Z, Xiaolei H, Lopresti D, Zhiyun X, Long R, Antani S, Thoma G (2009) A classifier ensemble based on performance level estimation. In: IEEE international symposium on biomedical imaging: from nano to macro. ISBI ’09, pp 342–345
12.
Artan Y, Huang X (2008) Combining multiple 2v-SVM classifiers for tissue segmentation. In: Proceedings of ISBI2008, pp 488–491
13.
Johansson U, Lofstrom T, Lofstrom T (2008) The problem with ranking ensembles based on training or validation performance. In: 2008 IEEE international joint conference on neural networks (IEEE world congress on computational intelligence), Hong Kong, pp 3222–3228
14.
Vannucci M, Colla V, Vannocci M, Nastasi G (2012) An ensemble classification method based on input clustering and classifiers expected reliability. In: Proceedings of 6th European modelling symposium on mathematical modelling and computer simulation EMS2012, Malta, November 14–16
15.
Kuncheva LI (2000) Clustering-and-selection model for classifier combination. In: Proceedings of fourth international conference on knowledge-based intelligent engineering systems and allied technologies. Brighton, vol 1, pp 185–188
16.
Colla V, Vannucci M, Allotta B, Malvezzi M (2003) Comparison of traditional and neural system for train speed estimation. In: Proceedings of the 11th European symposium on artificial neural networks ESANN 2004, vol 1, pp 401–406
17.
Duval-Poo MA, Sosa-García J, Guerra-Gandón A, Vega-Pons S, Ruiz-Shulcloper J (2012) A new classifier combination scheme using clustering ensemble. In: Progress in pattern recognition, image analysis, computer vision, and applications: 17th Iberoamerican congress, CIARP 2012, Buenos Aires, Argentina, September 3–6, pp 154–161
18.
Acharya A, Hruschka ER, Ghosh J, Acharyya S (2011) C3E: a framework for combining ensembles of classifiers and clusterers. In: Proceedings of 10th international workshop, MCS 2011, Naples, Italy, June 15–17, pp 269–278
19.
Masoudnia S, Ebrahimpour R, Arani SAAA (2012) Incorporation of a regularization term to control negative correlation in mixture of experts. Neural Process Lett 36(1):31–47CrossRef
20.
Loo CK, Liew WS, Seera M, Lim E (2014) Probabilistic ensemble Fuzzy ARTMAP optimization using hierarchical parallel genetic algorithms. Neural Comput Appl 26(2):263–276CrossRef
21.
Breiman L (1996) Bagging predictors. Mach Learn 24(2):123–140MATH
22.
Freund Y, Schapire R (1999) Experiments with a new boosting algorithm. In: Proceedings of the 13th international conference on machine learning. Bari, Italy, pp 148–156
23.
Avnimelech R, Intrator N (1999) Boosting regression estimators. Neural Comput 11:499CrossRef
24.
Karakoulas G, Shawe Taylor J (2000) Towards a strategy for boosting regressors. In: Smola A, Brattlet P, Scholkopf B, Schuurmans D (eds) Advances in large margin classifiers. MIT Press, Cambridge, p 247
25.
Riccardi A, Fernández-Navarro F, Carloni S (2014) Cost-sensitive AdaBoost algorithm for ordinal regression based on extreme learning machine. IEEE Trans Cybern 44(10):1898–1909CrossRef
26.
Fernández-Navarro F, Campoy-Munoz P, de la Paz-Marín M, Hervás-Martínez C, Yao X (2013) Addressing the EU sovereign ratings using an ordinal regression approach. IEEE Trans Cybern 43(6):2228–2240CrossRef
27.
Liu Y, Yao X (1999) Ensemble learning via negative correlation. Neural Netw 12(10):1399–1404CrossRef
28.
Reyneri LM, Colla V, Sgarbi M, Vannucci M (2009) Self-estimation of data and approximation reliability through neural networks. In: Proceedings of 10th international work-conference on artificial neural networks, IWANN 2009, Salamanca, Spain, June 10–12, 2009. Proceedings, Part I
29.
Haykin S (2009) Neural networks: a comprehensive foundation—chapter 9: self-organizing. Prentice-Hall, Englewood Cliffs. ISBN 0-13-908385-5
30.
Vannucci M, Colla V, Cateni S (2015) An hybrid ensemble method based on data clustering and weak learners reliabilities estimated through neural networks. In: Advances in computational intelligence. Lecture notes in computer science, vol 9095, pp 400–411
31.
Rencher AC, Christensen WF (2012) Chapter 10, multivariate regression, section 10.1, introduction. Methods of multivariate analysis, Wiley series in probability and statistics, vol 709, 3rd edn. Wiley, New York, p 19
32.
33.
Allotta B, Colla V, Malvezzi M (2002) Train position and speed estimation using wheel velocity measurements. Proc Inst Mech Eng F J Rail Rapid Transit 216(3):207–225CrossRef
Metadaten
Titel
Learners Reliability Estimated Through Neural Networks Applied to Build a Novel Hybrid Ensemble Method
verfasst von
Marco Vannucci
Valentina Colla
Silvia Cateni
Publikationsdatum
27.01.2017
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 3/2017
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-017-9586-6

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