메뉴 건너뛰기




Volumn 11, Issue 4, 2011, Pages 3690-3696

Assessing the contribution of variables in feed forward neural network

Author keywords

Multicollinearity; Network weights; Prediction; Regression; Relative importance; Simulation

Indexed keywords

MULTICOLLINEARITY; NETWORK WEIGHTS; PREDICTION; REGRESSION; RELATIVE IMPORTANCE; SIMULATION;

EID: 79954593174     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2011.01.040     Document Type: Article
Times cited : (52)

References (28)
  • 1
    • 0032526110 scopus 로고    scopus 로고
    • Epidemiologic interpretation of artificial neural networks
    • M.S. Duh, A.M. Walker, and J.Z. Ayanian Epidemiologic interpretation of artificial neural networks American Journal of Epidemiology 147 1998 1112 1122 (Pubitemid 28285453)
    • (1998) American Journal of Epidemiology , vol.147 , Issue.12 , pp. 1112-1122
    • Duh, M.-S.1    Walker, A.M.2    Ayanian, J.Z.3
  • 2
    • 0037102687 scopus 로고    scopus 로고
    • Illuminating the 'black box': A randomization approach for understanding variable contributions in artificial neural networks
    • DOI 10.1016/S0304-3800(02)00064-9, PII S0304380002000649
    • J.D. Olden, and D.A. Jackson Illuminating the "black box": a randomization approach for understanding variable contributions in artificial neural networks Ecological Modelling 154 2002 135 150 (Pubitemid 34793582)
    • (2002) Ecological Modelling , vol.154 , Issue.1-2 , pp. 135-150
    • Olden, J.D.1    Jackson, D.A.2
  • 3
    • 0345375364 scopus 로고    scopus 로고
    • Comparison of the performance of multi-layer perceptron and linear regression for epidemiological data
    • DOI 10.1016/S0167-9473(02)00257-8, PII S0167947302002578
    • J. Gaudart, B. Giusiano, and L. Huiart Comparison of the performance of multi-layer perceptron and linear regression for epidemiological data Computational Statistics and Data Analysis 44 2004 547 570 (Pubitemid 37471074)
    • (2004) Computational Statistics and Data Analysis , vol.44 , Issue.4 , pp. 547-570
    • Gaudart, J.1    Giusiano, B.2    Huiart, L.3
  • 4
    • 33744944043 scopus 로고    scopus 로고
    • Comparison of recent methods for inference of variable influence in neural networks
    • DOI 10.1016/j.neunet.2005.09.002, PII S0893608005002480
    • S. Papadokonstantakis, A. Lygeros, and S.V. Jacobsson Comparison of recent methods for inference of variable influence in neural networks Neural Networks 19 2006 500 513 (Pubitemid 43850235)
    • (2006) Neural Networks , vol.19 , Issue.4 , pp. 500-513
    • Papadokonstantakis, S.1    Lygeros, A.2    Jacobsson, S.P.3
  • 5
    • 34247525561 scopus 로고    scopus 로고
    • An approach for determining relative input parameter importance and significance in artificial neural networks
    • DOI 10.1016/j.ecolmodel.2007.01.009, PII S0304380007000245
    • S.J. Kemp, P. Zaradic, and F. Hansen An approach for determining relative input parameter importance and significance in artificial neural networks Ecological Modelling 204 2007 326 334 (Pubitemid 46654564)
    • (2007) Ecological Modelling , vol.204 , Issue.3-4 , pp. 326-334
    • Kemp, S.J.1    Zaradic, P.2    Hansen, F.3
  • 7
    • 54249085849 scopus 로고    scopus 로고
    • Prediction of local scour downstream of grade-control structures using neural networks
    • A. Guven, and M. Gunal Prediction of local scour downstream of grade-control structures using neural networks Journal of Hydraulic Engineering 134 11 2008 1656 1660
    • (2008) Journal of Hydraulic Engineering , vol.134 , Issue.11 , pp. 1656-1660
    • Guven, A.1    Gunal, M.2
  • 8
    • 0000671231 scopus 로고    scopus 로고
    • Ranking importance of input parameters of neural networks
    • A.H. Sung Ranking importance of input parameters of neural networks Expert Systems with Applications 15 1997 405 411
    • (1997) Expert Systems with Applications , vol.15 , pp. 405-411
    • Sung, A.H.1
  • 9
    • 0037442845 scopus 로고    scopus 로고
    • Review and comparison of methods to study the contribution of variables in artificial neural network models
    • DOI 10.1016/S0304-3800(02)00257-0, PII S0304380002002570
    • M. Gevrey, I. Dimopoulos, and S. Lek Review and comparison of methods to study the contribution of variables in artificial neural network models Ecological Modelling 160 2003 249 264 (Pubitemid 36251040)
    • (2003) Ecological Modelling , vol.160 , Issue.3 , pp. 249-264
    • Gevrey, M.1    Dimopoulos, I.2    Lek, S.3
  • 10
    • 3242721368 scopus 로고    scopus 로고
    • An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data
    • DOI 10.1016/j.ecolmodel.2004.03.013, PII S0304380004001565
    • J.D. Olden, M.K. Joy, and R.G. Death An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data Ecological Modelling 178 2004 389 397 (Pubitemid 38946674)
    • (2004) Ecological Modelling , vol.178 , Issue.3-4 , pp. 389-397
    • Olden, J.D.1    Joy, M.K.2    Death, R.G.3
  • 11
    • 40949093593 scopus 로고    scopus 로고
    • Using artificial neural networks to determine the relative contribution of abiotic factors influencing the establishment of insect pest species
    • DOI 10.1016/j.ecoinf.2007.06.004, PII S1574954107000398
    • M.J. Watts, and S.P. Worner Using artificial neural networks to determine the relative contribution of abiotic factors influencing the establishment of insect pest pecies Ecological Informatics 3 2008 64 74 (Pubitemid 351406517)
    • (2008) Ecological Informatics , vol.3 , Issue.1 , pp. 64-74
    • Watts, M.J.1    Worner, S.P.2
  • 14
    • 0040558027 scopus 로고    scopus 로고
    • A heuristic method for estimating the relative weight of predictor variables in multiple regression
    • J.W. Johnson A heuristic method for estimating the relative weight of predictor variables in multiple regression Multivariate Behavioral Research 35 2000 1 19
    • (2000) Multivariate Behavioral Research , vol.35 , pp. 1-19
    • Johnson, J.W.1
  • 15
    • 3042607736 scopus 로고    scopus 로고
    • A Monte Carlo comparison of relative importance methodologies
    • DOI 10.1177/1094428104266017
    • J.M. Lebreton, R.E. Ployhart, and R.T. Ladd A Monte Carlo comparison of relative importance methodologies Organizational Research Methods 7 3 2004 258 282 (Pubitemid 38835020)
    • (2004) Organizational Research Methods , vol.7 , Issue.3 , pp. 258-282
    • LeBreton, J.M.1    Ployhart, R.E.2    Ladd, R.T.3
  • 16
    • 12044256695 scopus 로고
    • Dominance analysis: A new approach to the problem of relative importance of predictors in multiple regression
    • D.V. Budescu Dominance analysis: a new approach to the problem of relative importance of predictors in multiple regression Psychological Bulletin 114 1993 542 551
    • (1993) Psychological Bulletin , vol.114 , pp. 542-551
    • Budescu, D.V.1
  • 17
    • 0141768106 scopus 로고    scopus 로고
    • The Dominance Analysis Approach for Comparing Predictors in Multiple Regression
    • DOI 10.1037/1082-989X.8.2.129
    • R. Azen, and D.V. Budescu The dominance analysis approach for comparing predictors in multiple regression Psychological Methods 8 2003 129 148 (Pubitemid 37206648)
    • (2003) Psychological Methods , vol.8 , Issue.2 , pp. 129-148
    • Azen, R.1    Budescu, D.V.2
  • 18
    • 3042640145 scopus 로고    scopus 로고
    • History and use of relative importance indices in organizational research
    • DOI 10.1177/1094428104266510
    • J.W. Johnson, and J.M. LeBreton History and use of relative importance indices in organizational research Organizational Research Methods 7 2004 238 257 (Pubitemid 38845329)
    • (2004) Organizational Research Methods , vol.7 , Issue.3 , pp. 238-257
    • Johnson, J.W.1    LeBreton, J.M.2
  • 19
    • 62749092713 scopus 로고
    • Sample size and the accuracy of predictions made from multiple regression equations
    • R. Sawyer Sample size and the accuracy of predictions made from multiple regression equations Journal of Educational Statistics 7 2 1982 91 104
    • (1982) Journal of Educational Statistics , vol.7 , Issue.2 , pp. 91-104
    • Sawyer, R.1
  • 22
    • 0000873069 scopus 로고
    • A method for the solution of certain problems in least squares
    • K. Levenberg A method for the solution of certain problems in least squares Quarterly Applied Mathematics 2 1944 164 168
    • (1944) Quarterly Applied Mathematics , vol.2 , pp. 164-168
    • Levenberg, K.1
  • 23
    • 0000169232 scopus 로고
    • An algorithm for least-squares estimation of nonlinear parameters
    • D.W. Marquardt An algorithm for least-squares estimation of nonlinear parameters Journal of Society of Industrial Mathematics 11 1963 431 441
    • (1963) Journal of Society of Industrial Mathematics , vol.11 , pp. 431-441
    • Marquardt, D.W.1
  • 26
    • 0028543366 scopus 로고
    • Training feedforward networks with the Marquardt algorithm
    • M.T. Hagan, and M. Menhaj Training feedforward networks with the Marquardt algorithm IEEE Transactions on Neural Networks 5 6 1994 989 993
    • (1994) IEEE Transactions on Neural Networks , vol.5 , Issue.6 , pp. 989-993
    • Hagan, M.T.1    Menhaj, M.2
  • 27
    • 0030342538 scopus 로고    scopus 로고
    • Parental Psychological Control: Revisiting a Neglected Construct
    • B.K. Barber Parental psychological control: revisiting a neglected construct Child Development 67 1996 3296 3319 (Pubitemid 126416778)
    • (1996) Child Development , vol.67 , Issue.6 , pp. 3296-3319
    • Barber, B.K.1
  • 28
    • 33746115266 scopus 로고    scopus 로고
    • Comparing predictors in multivariate regression models: An extension of dominance analysis
    • DOI 10.3102/10769986031002157
    • R. Azen, and D.V. Budescu Comparing predictors in multivariate regression models: an extension of dominance analysis Journal of Educational and Behavioral Statistics 31 2 2006 157 180 (Pubitemid 44085345)
    • (2006) Journal of Educational and Behavioral Statistics , vol.31 , Issue.2 , pp. 157-180
    • Azen, R.1    Budescu, D.V.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.