{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T04:29:42Z","timestamp":1777696182711,"version":"3.51.4"},"reference-count":47,"publisher":"SAGE Publications","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IDA"],"published-print":{"date-parts":[[2022,4,18]]},"abstract":"<jats:p>Existing correlation processing strategies make up for the defect that most evaluation algorithms do not consider the independence between indicators. However, these solutions may change the indicator system\u2019s internal connection, affecting the final evaluation result\u2019s interpretability and accuracy. Besides, traditional independent analysis methods cannot accurately describe the complex multivariate correlation based on the linear relationship. Aimed at these problems, we propose an indicators correlation elimination algorithm based on the feedforward neural network and Taylor expansion (NNTE). Firstly, we propose a generalized n-power correlation and a feedforward neural network to express the relationship between indicators quantitatively. Secondly, the low-order Taylor expression expanded at every sample is pointed to eliminate nonlinear relationships. Finally, to control the expansions\u2019 accuracy, the layer-by-layer stripping method is presented to reduce the dimensionality of the correlations among multiple indicators gradually. This procedure continues to iterate until there are all simple two-dimensional correlations, eliminating multiple variables\u2019 correlations. To compare the elimination efficiency, the ranking accuracy is proposed to measure the distance of the resulting sequence to the benchmark sequence. Under Cleveland and KDD99 two datasets, the ranking accuracy of the NNTE method is 71.64% and 96.41%, respectively. Compared with other seven common elimination methods, our proposed method\u2019s average increase is 13.67% and 25.13%, respectively.<\/jats:p>","DOI":"10.3233\/ida-215955","type":"journal-article","created":{"date-parts":[[2022,4,26]],"date-time":"2022-04-26T13:27:07Z","timestamp":1650979627000},"page":"751-783","source":"Crossref","is-referenced-by-count":3,"title":["An integrated model based on feedforward neural network and Taylor expansion for indicator correlation elimination"],"prefix":"10.1177","volume":"26","author":[{"given":"Wei","family":"Guo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Han","family":"Qiu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zimian","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junhu","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingxian","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/IDA-215955_ref1","first-page":"57","article-title":"Fast ICA for noisy data using gaussian moments","volume":"5","author":"Hyvarinen","year":"1999","journal-title":"Proc. \u2013 IEEE Int. Symp. Circuits Syst."},{"key":"10.3233\/IDA-215955_ref2","doi-asserted-by":"crossref","unstructured":"A. Stasinopoulos, C. Ntantogian and C. Xenakis, Commix: Automating evaluation and exploitation of command injection vulnerabilities in Web applications, Int. J. Inf. Secur. 18(1) (2019).","DOI":"10.1007\/s10207-018-0399-z"},{"key":"10.3233\/IDA-215955_ref3","doi-asserted-by":"crossref","unstructured":"B. Billah, M.L. King, R.D. Snyder and A.B. Koehler, Exponential smoothing model selection for forecasting, Int. J. Forecast. 22(2) (2006).","DOI":"10.1016\/j.ijforecast.2005.08.002"},{"issue":"5","key":"10.3233\/IDA-215955_ref4","first-page":"2757","article-title":"Distance multivariance: New dependence measures for random vectors","volume":"47","author":"B\u00a8ttcher","year":"2019","journal-title":"Ann. Stat."},{"key":"10.3233\/IDA-215955_ref6","doi-asserted-by":"crossref","unstructured":"C. Perrotta and B. Williamson, The social life of Learning Analytics: Cluster analysis and the \u201cperformance\u201d of algorithmic education, Learn. Media Technol. 43(1) (2018).","DOI":"10.1080\/17439884.2016.1182927"},{"key":"10.3233\/IDA-215955_ref7","doi-asserted-by":"crossref","unstructured":"C.T. Fitz-Gibbon, Multilevel Modelling in an Indicator System, in: Schools, Classrooms, and Pupils, London, 1991, pp. 67\u201383.","DOI":"10.1016\/B978-0-12-582910-6.50011-3"},{"issue":"3","key":"10.3233\/IDA-215955_ref8","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1162\/COLI_a_00293","article-title":"A Kernel independence test for geographical language variation","volume":"43","author":"Nguyen","year":"2017","journal-title":"Comput. Linguist."},{"issue":"518","key":"10.3233\/IDA-215955_ref9","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1080\/01621459.2016.1150851","article-title":"Independent component analysis via distance covariance","volume":"112","author":"Matteson","year":"2017","journal-title":"J. Am. Stat. Assoc."},{"key":"10.3233\/IDA-215955_ref13","doi-asserted-by":"crossref","unstructured":"G. Fan, D. Zhong, F. Yan and P. Yue, A hybrid fuzzy evaluation method for curtain grouting efficiency assessment based on an AHP method extended by D numbers, Expert Syst. Appl. 44 (2016).","DOI":"10.1016\/j.eswa.2015.09.006"},{"issue":"3","key":"10.3233\/IDA-215955_ref14","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.knosys.2010.01.003","article-title":"GRA method for multiple attribute decision making with incomplete weight information in intuitionistic fuzzy setting","volume":"23","author":"Wei","year":"2010","journal-title":"Knowledge-Based Syst."},{"issue":"4\u20135","key":"10.3233\/IDA-215955_ref15","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1007\/BF00332918","article-title":"Auto-association by multilayer perceptrons and singular value decomposition","volume":"59","author":"Bourlard","year":"1988","journal-title":"Biol. Cybern."},{"key":"10.3233\/IDA-215955_ref16","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.csda.2019.05.004","article-title":"A distribution-free test of independence based on mean variance index","volume":"139","author":"Cui","year":"2019","journal-title":"Comput. Stat. Data Anal."},{"key":"10.3233\/IDA-215955_ref17","doi-asserted-by":"crossref","unstructured":"H. Peng, F. Long and C. Ding, Feature selection based on mutual information: Criteria of max-dependency, max-relevance, and min-redundancy, IEEE Trans. Pattern Anal. Mach. Intell. 27(8) (2005).","DOI":"10.1109\/TPAMI.2005.159"},{"key":"10.3233\/IDA-215955_ref18","doi-asserted-by":"crossref","unstructured":"I.Y. Kim and O.L. De Weck, Adaptive weighted-sum method for bi-objective optimization: Pareto front generation, Struct. Multidiscip. Optim. 29(2) (2005).","DOI":"10.1007\/s00158-004-0465-1"},{"issue":"339","key":"10.3233\/IDA-215955_ref20","doi-asserted-by":"crossref","first-page":"578","DOI":"10.1080\/01621459.1972.10481251","article-title":"Significance testing of the spearman rank correlation coefficient","volume":"67","author":"Zar","year":"1972","journal-title":"J. Am. Stat. Assoc."},{"key":"10.3233\/IDA-215955_ref21","doi-asserted-by":"crossref","unstructured":"J. Jang-Jaccard and S. Nepal, A survey of emerging threats in cybersecurity, in: Journal of Computer and System Sciences, Vol. 80, no. 5, 2014.","DOI":"10.1016\/j.jcss.2014.02.005"},{"key":"10.3233\/IDA-215955_ref22","doi-asserted-by":"crossref","unstructured":"J. Li et al., Feature selection: A data perspective, ACM Computing Surveys 50(6) (2017).","DOI":"10.1145\/3136625"},{"issue":"3","key":"10.3233\/IDA-215955_ref23","first-page":"74","article-title":"Rank Correlation Methods","volume":"20","author":"Bevan","year":"1971","journal-title":"Stat."},{"issue":"2","key":"10.3233\/IDA-215955_ref25","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/S0167-8809(01)00272-9","article-title":"Indicator quality for assessment of impact of multidisciplinary systems","volume":"87","author":"Riley","year":"2001","journal-title":"Agric. Ecosyst. Environ."},{"key":"10.3233\/IDA-215955_ref26","doi-asserted-by":"crossref","unstructured":"J. Wang and Y. bin Hou, Packet loss rate mapped to the quality of experience, Multimed. Tools Appl. 77(1) (2018).","DOI":"10.1007\/s11042-016-4254-9"},{"key":"10.3233\/IDA-215955_ref27","doi-asserted-by":"crossref","unstructured":"J. Zhu and M. Collette, A dynamic discretization method for reliability inference in Dynamic Bayesian Networks, Reliab. Eng. Syst. Saf. 138 (2015).","DOI":"10.1016\/j.ress.2015.01.017"},{"key":"10.3233\/IDA-215955_ref28","doi-asserted-by":"crossref","unstructured":"K.P. Yoon and W.K. Kim, The behavioral TOPSIS, Expert Syst. Appl. 89 (2017).","DOI":"10.1016\/j.eswa.2017.07.045"},{"issue":"5","key":"10.3233\/IDA-215955_ref29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.5121\/ijdkp.2011.1501","article-title":"Analysis of heart diseases dataset using neural network approach","volume":"1","author":"Rani","year":"2011","journal-title":"Int. J. Data Min. Knowl. Manag. Process"},{"key":"10.3233\/IDA-215955_ref30","doi-asserted-by":"crossref","unstructured":"K. Zheng, X. Wang, B. Wu and T. Wu, Feature subset selection combining maximal information entropy and maximal information coefficient, Appl. Intell. 50(2) (2020).","DOI":"10.1007\/s10489-019-01537-x"},{"key":"10.3233\/IDA-215955_ref31","doi-asserted-by":"crossref","unstructured":"\u0141. Apiecionek, J.M. Czerniak and W.T. Dobrosielski, Quality of services method as a DDoS protection tool, Adv. Intell. Syst. Comput. 323 (2015).","DOI":"10.1007\/978-3-319-11310-4_20"},{"issue":"6","key":"10.3233\/IDA-215955_ref32","doi-asserted-by":"crossref","first-page":"2537","DOI":"10.1214\/15-AOS1351","article-title":"Bootstrap and permutation tests of independence for point processes","volume":"43","author":"Albert","year":"2015","journal-title":"Ann. Stat."},{"key":"10.3233\/IDA-215955_ref33","doi-asserted-by":"crossref","unstructured":"M. Fischlin, A cost-effective pay-per-multiplication comparison method for millionaires, in: Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 2020, 2001.","DOI":"10.1007\/3-540-45353-9_33"},{"key":"10.3233\/IDA-215955_ref34","doi-asserted-by":"crossref","unstructured":"M.H. Bhuyan, D.K. Bhattacharyya and J.K. Kalita, An empirical evaluation of information metrics for low-rate and high-rate DDoS attack detection, Pattern Recognit. Lett. 51 (2015).","DOI":"10.1016\/j.patrec.2014.07.019"},{"key":"10.3233\/IDA-215955_ref35","doi-asserted-by":"crossref","first-page":"1335","DOI":"10.1111\/j.1467-8659.2012.03125.x","article-title":"A taxonomy of visual cluster separation factors","volume":"31","author":"Sedlmair","year":"2012","journal-title":"Comput. Graph. Forum"},{"issue":"3","key":"10.3233\/IDA-215955_ref37","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/0165-1684(94)90029-9","article-title":"Independent component analysis, A new concept","volume":"36","author":"Comon","year":"1994","journal-title":"Signal Processing"},{"key":"10.3233\/IDA-215955_ref38","doi-asserted-by":"crossref","unstructured":"P.H. Dos Santos, S.M. Neves, D.O. Sant\u2019Anna, C.H. de Oliveira and H.D. Carvalho, The analytic hierarchy process supporting decision making for sustainable development: An overview of applications, Journal of Cleaner Production 212 (2019).","DOI":"10.1016\/j.jclepro.2018.11.270"},{"key":"10.3233\/IDA-215955_ref39","doi-asserted-by":"crossref","unstructured":"Pearson Karl, VII. Mathematical contributions to the theory of evolution. \u2013 III. Regression, heredity, and panmixia, Philos. Trans. R. Soc. London. Ser. A, Contain. Pap. a Math. or Phys. Character 187 (1896), 253\u2013318.","DOI":"10.1098\/rsta.1896.0007"},{"key":"10.3233\/IDA-215955_ref40","doi-asserted-by":"crossref","unstructured":"R. Ginevi\u010dius, V. Podvezko and D. Mikelis, Quantitative evaluation of economic and social development of lithuanian regions, Ekonomika 65 (2004).","DOI":"10.15388\/Ekon.2004.17341"},{"key":"10.3233\/IDA-215955_ref41","doi-asserted-by":"crossref","unstructured":"R. Rajesh and V. Ravi, Supplier selection in resilient supply chains: A grey relational analysis approach, J. Clean. Prod. 86 (2015).","DOI":"10.1016\/j.jclepro.2014.08.054"},{"key":"10.3233\/IDA-215955_ref42","doi-asserted-by":"crossref","unstructured":"Ramadiani, B. Ramadhani, Z. Arifin, M.L. Jundillah and A. Azainil, Decision support system for determining Chili land using weighted product method, Bull. Electr. Eng. Informatics 9(3) (2020).","DOI":"10.11591\/eei.v9i3.2004"},{"issue":"919","key":"10.3233\/IDA-215955_ref43","doi-asserted-by":"crossref","first-page":"1015","DOI":"10.1086\/668105","article-title":"Principal component analysis with noisy and\/or missing data","volume":"124","author":"Bailey","year":"2012","journal-title":"Publ. Astron. Soc. Pacific"},{"issue":"1\u20133","key":"10.3233\/IDA-215955_ref45","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/0169-7439(87)80084-9","article-title":"Principal component analysis","volume":"2","author":"Wold","year":"1987","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"10.3233\/IDA-215955_ref46","doi-asserted-by":"crossref","unstructured":"S. Yu, Y. Tian, S. Guo and D.O. Wu, Can we beat DDoS attacks in clouds, IEEE Trans. Parallel Distrib. Syst. 25(9) (2014).","DOI":"10.1109\/TPDS.2013.181"},{"issue":"3","key":"10.3233\/IDA-215955_ref47","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1093\/biomet\/asz024","article-title":"Nonparametric independence testing via mutual information","volume":"106","author":"Berrett","year":"2019","journal-title":"Biometrika"},{"issue":"7","key":"10.3233\/IDA-215955_ref48","doi-asserted-by":"crossref","first-page":"1641","DOI":"10.1162\/089976603321891846","article-title":"Approximation by fully complex multilayer perceptrons","volume":"15","author":"Kim","year":"2003","journal-title":"Neural Comput."},{"key":"10.3233\/IDA-215955_ref49","doi-asserted-by":"crossref","unstructured":"U.N. Dulhare, Prediction system for heart disease using Naive Bayes and particle swarm optimization, Biomed. Res. 29(12) (2018).","DOI":"10.4066\/biomedicalresearch.29-18-620"},{"key":"10.3233\/IDA-215955_ref51","doi-asserted-by":"crossref","unstructured":"V. Tiwari, P.K. Jain and P. Tandon, Product design concept evaluation using rough sets and VIKOR method, Adv. Eng. Informatics 30(1) (2016).","DOI":"10.1016\/j.aei.2015.11.005"},{"key":"10.3233\/IDA-215955_ref52","doi-asserted-by":"crossref","unstructured":"W. Becker, M. Saisana, P. Paruolo and I. Vandecasteele, Weights and importance in composite indicators: Closing the gap, Ecol. Indic. 80 (2017).","DOI":"10.1016\/j.ecolind.2017.03.056"},{"key":"10.3233\/IDA-215955_ref53","unstructured":"W.J. Ou and X.Y. Fang, Assessment of black-start modes based on entropy value method and principal component analysis, Dianli Xitong Baohu yu Kongzhi\/Power Syst. Prot. Control 42(8) (2014)."},{"key":"10.3233\/IDA-215955_ref55","doi-asserted-by":"crossref","unstructured":"Y.M. Wang and T.M.S. Elhag, A goal programming method for obtaining interval weights from an interval comparison matrix, Eur. J. Oper. Res. 177(1) (2007).","DOI":"10.1016\/j.ejor.2005.10.066"},{"key":"10.3233\/IDA-215955_ref56","doi-asserted-by":"crossref","unstructured":"Z. Jin and D.S. Matteson, Generalizing distance covariance to measure and test multivariate mutual dependence via complete and incomplete V-statistics, J. Multivar. Anal. 168 (2018).","DOI":"10.1016\/j.jmva.2018.08.006"},{"key":"10.3233\/IDA-215955_ref57","doi-asserted-by":"crossref","first-page":"106959","DOI":"10.1016\/j.csda.2020.106959","article-title":"Bayesian nonparametric test for independence between random vectors","volume":"149","author":"Ma","year":"2020","journal-title":"Comput. Stat. Data Anal."}],"container-title":["Intelligent Data Analysis"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/IDA-215955","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:19:29Z","timestamp":1777454369000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/IDA-215955"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,18]]},"references-count":47,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.3233\/ida-215955","relation":{},"ISSN":["1088-467X","1571-4128"],"issn-type":[{"value":"1088-467X","type":"print"},{"value":"1571-4128","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,18]]}}}