Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 5/2019

30.01.2018 | Original Article

Per-sample prediction intervals for extreme learning machines

verfasst von: Anton Akusok, Yoan Miche, Kaj-Mikael Björk, Amaury Lendasse

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 5/2019

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Prediction intervals in supervised machine learning bound the region where the true outputs of new samples may fall. They are necessary in the task of separating reliable predictions of a trained model from near random guesses, minimizing the rate of false positives, and other problem-specific tasks in applied machine learning. Many real problems have heteroscedastic stochastic outputs, which explains the need of input-dependent prediction intervals. This paper proposes to estimate the input-dependent prediction intervals by a separate extreme learning machine model, using variance of its predictions as a correction term accounting for the model uncertainty. The variance is estimated from the model’s linear output layer with a weighted Jackknife method. The methodology is very fast, robust to heteroscedastic outputs, and handles both extremely large datasets and insufficient amount of training data.
Vorheriger Artikel Kernel extreme learning machine based on fuzzy set theory for multi-label classification
Nächster Artikel Friend recommendation in social networks based on multi-source information fusion

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

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!

Jetzt informieren

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!

Jetzt informieren

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!

Jetzt informieren
Weitere Produktempfehlungen anzeigen
Literatur
1.
Akusok A, Miche Y, Hegedus J, Nian R, Lendasse A (2014) A two-stage methodology using K-NN and false-positive minimizing ELM for nominal data classification. Cognit Comput 6(3):432–445CrossRef
2.
Hegedus J, Miche Y, Ilin A, Lendasse A (2011) Methodology for Behavioral-based Malware Analysis and Detection Using Random Projections and K-Nearest Neighbors Classifiers. In: 2011 seventh international conference on computational intelligence and security, pp 1016–1023
3.
Pevec D, Kononenko I (2014) Input dependent prediction intervals for supervised regression. Intell Data Anal 18(5):873–887CrossRef
4.
Akusok A, Miche Y, Karhunen J, Björk KM, Nian R, Lendasse A (2015) Arbitrary category classification of websites based on image content. IEEE Comput Intell Mag 10(2):30–41CrossRef
5.
Huang GB, Zhu QY, Siew CK (2004) Extreme learning machine: A new learning scheme of feedforward neural networks. In: 2004 IEEE international joint conference on neural networks, 2004. Proceedings, vol 2, pp 985–990
6.
Lendasse A, Man VC, Miche Y, Huang GB (2016) Advances in extreme learning machines (ELM2014). Neurocomputing 174, Part A:1–3CrossRef
7.
Huang GB, Chen L, Siew CK (2006) Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Trans Neural Netw 17(4):879–892CrossRef
8.
Tikhonov AN (1963) Solution of incorrectly formulated problems and the regularization method. Sov Math Dokl 5:1035–1038MATH
9.
Miche Y, van Heeswijk M, Bas P, Simula O, Lendasse A. (2011)TROP-ELM: a double-regularized ELM using LARS and Tikhonov regularization. In: Advances in extreme learning machine: theory and applications biological inspired systems. Computational and ambient intelligence selected papers of the 10th international work-conference on artificial neural networks (IWANN2009), vol 74(16), pp 2413–2421
10.
Akusok A, Veganzones D, Miche Y, Björk KM, du Jardin P, Séverin E, Lendasse A (2015) MD-ELM: originally mislabeled samples detection using OP-ELM model. Neurocomputing 159:242–250CrossRef
11.
Termenon M, Graña M, Savio A, Akusok A, Miche Y, Björk KM, Lendasse A (2016) Brain MRI morphological patterns extraction tool based on extreme learning machine and majority vote classification. Neurocomputing 174, Part A:344–351CrossRef
12.
Huang GB, Bai Z, Kasun L, Vong CM (2015) Local receptive fields based extreme learning machine. IEEE Comput Intell Mag 10(2):18–29CrossRef
13.
Sovilj D, Eirola E, Miche Y, Björk KM, Nian R, Akusok A, Lendasse A (2016) Extreme learning machine for missing data using multiple imputations. Neurocomputing 174, Part A:220–231CrossRef
14.
Huang Z, Yu Y, Gu J, Liu H (2017) An efficient method for traffic sign recognition based on extreme learning machine. IEEE Trans Cybern 47(4):920–933CrossRef
15.
Akusok A, Björk KM, Miche Y, Lendasse A (2015) High-performance extreme learning machines: a complete toolbox for big data applications. IEEE Access 3:1011–1025CrossRef
16.
Swaney C, Akusok A, Björk KM, Miche Y, Lendasse A (2015) Efficient skin segmentation via neural networks: HP-ELM and BD-SOM. In: INNS conference on big data 2015 program, San Francisco, CA, USA 8–10 Aug 2015, vol 53, pp 400–409
17.
Soria-Olivas E, Gomez-Sanchis J, Martin JD, Vila-Frances J, Martinez M, Magdalena JR, Serrano AJ (2011) BELM: Bayesian extreme learning machine. IEEE Trans Neural Netw 22(3):505–509CrossRef
18.
Chen Y, Yang J, Wang C, Park D (2016) Variational Bayesian extreme learning machine. Neural Comput Appl 27(1):185–196CrossRef
19.
Shang Z, He J (2015) Confidence-weighted extreme learning machine for regression problems. Neurocomputing 148:544–550CrossRef
20.
He YL, Wang XZ, Huang JZ (2016) Fuzzy nonlinear regression analysis using a random weight network. Inf Sci 364(C):222–240CrossRef
21.
Asai H, Tanaka S, Uegima K (1982) Linear regression analysis with fuzzy model. IEEE Trans Syst Man Cybern 12(6):903–07MATHCrossRef
22.
Wang XZ, Xing HJ, Li Y, Hua Q, Dong CR, Pedrycz W (2015) A study on relationship between generalization abilities and fuzziness of base classifiers in ensemble learning. IEEE Trans Fuzzy Syst 23(5):1638–1654CrossRef
23.
Wang XZ, Zhang T, Wang R (2017) Noniterative deep learning: incorporating restricted boltzmann machine into multilayer random weight neural networks. IEEE Trans Syst Man Cybern Syst PP(99):1–10
24.
Ashfaq RAR, Wang XZ, Huang JZ, Abbas H, He YL (2017) Fuzziness based semi-supervised learning approach for intrusion detection system. Inf Sci 378:484–497CrossRef
25.
Pevec D, Kononenko I (2015) Prediction intervals in supervised learning for model evaluation and discrimination. Appl Intell 42(4):790–804CrossRef
26.
27.
Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. In: Neural networks selected papers from the 7th Brazilian symposium on neural networks (SBRN ’04), vol 70(1–3), pp 489–501
28.
Rao CR, Mitra SK (1972) Generalized inverse of a matrix and its applications. In: Proceedings of the sixth Berkeley symposium on mathematical statistics and probability. Theory of statistics, vol 1. University of California Press, Berkeley, pp 601–620
29.
Huang GB, Zhou H, Ding X, Zhang R (2012) Extreme learning machine for regression and multiclass classification. Syst Man Cybern Part B Cybern IEEE Trans 42(2):513–529CrossRef
30.
Bishop CM (2006) Pattern recognition and machine learning. Information science and statistics, vol 4. Springer Science + Business Media, SingaporeMATH
31.
Shao J, Wu CFJ (1987) Heteroscedasticity-robustness of Jackknife variance estimators in linear models. Ann Stat 15(4):1563–1579MathSciNetMATHCrossRef
32.
Loève M (1955) Probability Theory; foundations. Random Sequences. D. Van Nostrand Company, New YorkMATH
33.
Johnson RA, Wichern DW (2002) Applied multivariate statistical analysis, vol 5. Prentice Hall, Upper Saddle RiverMATH
34.
Nix DA, Weigend AS (1995) Learning local error bars for nonlinear regression. In: Tesauro G, Touretzky DS, Leen TK (eds) Advances in neural information processing systems, vol 7. MIT Press, Cambridge, pp 489–496
35.
36.
Horn PS, Pesce AJ, Copeland BE (1998) A robust approach to reference interval estimation and evaluation. Clin Chem 44(3):622–631
37.
Flachaire E (2005) Bootstrapping heteroskedastic regression models: wild bootstrap vs. pairs bootstrap. In: 2nd CSDA special issue on computational econometrics, vol 49(2), pp 361–376
38.
39.
Khosravi A, Nahavandi S, Creighton D, Atiya AF (2011) Lower upper bound estimation method for construction of neural network-based prediction intervals. IEEE Trans Neural Netw 22(3):337–346CrossRef
40.
Yeh IC (1998) Modeling of strength of high-performance concrete using artificial neural networks. Cem Concr Res 28(12):1797–1808CrossRef
41.
Nierenberg DW, Stukel TA, Baron JA, Dain BJ, Greenberg ER (1989) Determinants of plasma levels of beta-carotene and retinol. Am J Epidemiol 130(3):511–521CrossRef
42.
Guidorzi R, Rossi R (1974) Identification of a power plant from normal operating records. Autom Control Theory Appl 2(3):63–67
43.
Chryssolouris G, Lee M, Ramsey A (1996) Confidence interval prediction for neural network models. IEEE Trans Neural Netw 7(1):229–232CrossRef
44.
Ding AA, He X (2003) Backpropagation of pseudo-errors: neural networks that are adaptive to heterogeneous noise. IEEE Trans Neural Netw 14(2):253–262CrossRef
45.
MacKay DJC (1992) The evidence framework applied to classification networks. Neural Comput 4(5):720–736CrossRef
46.
Miche Y, Sorjamaa A, Bas P, Simula O, Jutten C, Lendasse A (2010) OP-ELM: optimally-pruned extreme learning machine. IEEE Trans Neural Netw 21(1):158–162CrossRef
47.
Zhu H, Tsang EC, Wang XZ, Ashfaq RAR (2017) Monotonic classification extreme learning machine. Neurocomputing 225(Supplement C):205–213CrossRef
48.
Phung SL, Bouzerdoum A, Chai DS (2005) Skin segmentation using color pixel classification: analysis and comparison. Pattern Anal Mach Intell IEEE Trans 27(1):148–154CrossRef
Metadaten
Titel
Per-sample prediction intervals for extreme learning machines
verfasst von
Anton Akusok
Yoan Miche
Kaj-Mikael Björk
Amaury Lendasse
Publikationsdatum
30.01.2018
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 5/2019
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-017-0777-2

Weitere Artikel der Ausgabe 5/2019

International Journal of Machine Learning and Cybernetics 5/2019 Zur Ausgabe

Original Article

A new global robust stability condition for uncertain neural networks with discrete and distributed delays

Original Article

Roughness measure based on description ability for attribute reduction in information system

Original Article

Constrained nonnegative matrix factorization-based semi-supervised multilabel learning

Original Article

An efficient automatic multiple objectives optimization feature selection strategy for internet text classification

Original Article

Global stability analysis of delayed complex-valued fractional-order coupled neural networks with nodes of different dimensions

Original Article

Graph-regularized multi-view semantic subspace learning

Neuer Inhalt

    Bildnachweise