{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T08:23:44Z","timestamp":1743150224791,"version":"3.40.3"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030304928"},{"type":"electronic","value":"9783030304935"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The availability of BIG molecular databases derived from quantum mechanics computations represent an opportunity for computational intelligence practitioners to develop new tools with same accuracy but much lower computational complexity compared to the costly Schr\u00f6dinger equation. In this study, unsupervised and supervised learning methods are applied to investigate the internal structure of the data and to learn the mapping between the atomic coordinates of molecules and their properties. Low dimensional spaces revealed a well defined clustering structure as defined by the measures used for comparing molecules based their atom distributions and chemical composition. Supervised learning techniques were applied on the original predictor variables, as well as on a subset of selected variables found using evolutionary algorithms guided by residual variance analysis (Gamma Test). Black and white box modeling approaches were used (random forests, neural networks and model trees and adaptive regression respectively). All of them delivered good performance, error and correlation-wise, with neural networks producing the best results. In particular white box techniques obtained explicit functional dependencies, some of them achieving considerably reduction of the feature set and expressed as simple models.<\/jats:p>","DOI":"10.1007\/978-3-030-30493-5_72","type":"book-chapter","created":{"date-parts":[[2019,9,10]],"date-time":"2019-09-10T20:03:41Z","timestamp":1568145821000},"page":"771-782","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Characterization of Quantum Derived Electronic Properties of Molecules: A Computational Intelligence Approach"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2930-0325","authenticated-orcid":false,"given":"Julio J.","family":"Vald\u00e9s","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8619-9441","authenticated-orcid":false,"given":"Alain B.","family":"Tchagang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,9,9]]},"reference":[{"issue":"9","key":"72_CR1","doi-asserted-by":"publisher","first-page":"751","DOI":"10.1002\/minf.201180003","volume":"30","author":"P Baldi","year":"2011","unstructured":"Baldi, P., M\u00fcller, K.R., Schneider, G.: Charting chemical space: challenges and opportunities for artificial intelligence and machine learning. Mol. Inf. 30(9), 751 (2011)","journal-title":"Mol. Inf."},{"key":"72_CR2","doi-asserted-by":"publisher","first-page":"1058","DOI":"10.1002\/qua.24954","volume":"115","author":"M Rupp","year":"2015","unstructured":"Rupp, M.: Machine learning for quantum mechanics in a nutshell. Int. J. Quantum Chem. 115, 1058\u20131073 (2015)","journal-title":"Int. J. Quantum Chem."},{"issue":"9","key":"72_CR3","doi-asserted-by":"publisher","first-page":"095003","DOI":"10.1088\/1367-2630\/15\/9\/095003","volume":"15","author":"G Montavon","year":"2013","unstructured":"Montavon, G., et al.: Machine learning of molecular electronic properties in chemical compound space. New J. Phys. 15(9), 095003 (2013)","journal-title":"New J. Phys."},{"key":"72_CR4","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1016\/j.commatsci.2014.10.032","volume":"98","author":"E Belisle","year":"2015","unstructured":"Belisle, E., Huang, Z., Le Digabel, S., Gheribi, A.E.: Evaluation of machine learning interpolation techniques for prediction of physical properties. Comput. Mater. Sci. 98, 170\u2013177 (2015)","journal-title":"Comput. Mater. Sci."},{"issue":"1","key":"72_CR5","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Mach. Learn. 45(1), 5\u201332 (2001)","journal-title":"Mach. Learn."},{"key":"72_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1098\/rspa.2001.0876","volume":"458","author":"D Evans","year":"2002","unstructured":"Evans, D., Jones, A.J.: A proof of the gamma test. Proc. Roy. Soc. Lond. A 458, 1\u201341 (2002)","journal-title":"Proc. Roy. Soc. Lond. A"},{"issue":"1","key":"72_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1214\/aos\/1176347963","volume":"19","author":"J Friedman","year":"1991","unstructured":"Friedman, J.: Multivariate adaptive regression splines. Ann. Stat. 19(1), 1\u201367 (1991)","journal-title":"Ann. Stat."},{"key":"72_CR8","unstructured":"Friedman, J.: Fast mars. Technical Report 110, Stanford University, Department of Statistics (1993)"},{"key":"72_CR9","volume-title":"Clustering Algorithms","author":"JA Hartigan","year":"1975","unstructured":"Hartigan, J.A.: Clustering Algorithms. Wiley, New York (1975)"},{"key":"72_CR10","unstructured":"Hinton, G.E., Roweis, S.T.: Stochastic neighbor embedding. In: Advances in Neural Information Processing Systems, vol. 15, pp. 833\u2013840 (2002)"},{"key":"72_CR11","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/3-540-46695-9_1","volume-title":"Advanced Topics in Artificial Intelligence","author":"G Holmes","year":"1999","unstructured":"Holmes, G., Hall, M., Prank, E.: Generating rule sets from model trees. In: Foo, N. (ed.) AI 1999. LNCS (LNAI), vol. 1747, pp. 1\u201312. Springer, Heidelberg (1999). https:\/\/doi.org\/10.1007\/3-540-46695-9_1"},{"key":"72_CR12","doi-asserted-by":"crossref","unstructured":"Jones, A.J., Evans, D., Margetts, S., Durrant, P.: The Gamma Test. In: Sarker, R., Abbass, H., Newton, S. (eds.) Heuristic and Optimization for Knowledge Discovery. Idea group Publishing (2002)","DOI":"10.4018\/978-1-930708-26-6.ch009"},{"key":"72_CR13","volume-title":"Combining Pattern Classifiers, Methods and Algorithms","author":"LI Kuncheva","year":"2005","unstructured":"Kuncheva, L.I.: Combining Pattern Classifiers, Methods and Algorithms. Wiley, New York (2005)"},{"key":"72_CR14","unstructured":"Quinlan, J.R.: Learning with continuous classes. In: 5th Australian Joint Conference on Artificial Intelligence, Singapore, pp. 343\u2013348 (1992)"},{"issue":"5","key":"72_CR15","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1109\/T-C.1969.222678","volume":"C\u201318","author":"JW Sammon","year":"1969","unstructured":"Sammon, J.W.: A nonlinear mapping for data structure analysis. IEEE Trans. Comput. C\u201318(5), 401\u2013409 (1969)","journal-title":"IEEE Trans. Comput."},{"issue":"3","key":"72_CR16","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1007\/BF01413858","volume":"5","author":"Adoalbj\u00f6rn Stef\u00e1nsson","year":"1997","unstructured":"Stef\u00e1nsson, A., Kon\u010dar, N., Jones, A.J.: A note on the gamma test. Neural Comput. Appl. 5, 131\u2013133 (1997)","journal-title":"Neural Computing & Applications"},{"key":"72_CR17","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"615","DOI":"10.1007\/3-540-39205-X_101","volume-title":"Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing","author":"JJ Vald\u00e9s","year":"2003","unstructured":"Vald\u00e9s, J.J.: Virtual reality representation of information systems and decision rules: an exploratory technique for understanding data and knowledge structure. In: Wang, G., Liu, Q., Yao, Y., Skowron, A. (eds.) RSFDGrC 2003. LNCS (LNAI), vol. 2639, pp. 615\u2013618. Springer, Heidelberg (2003). https:\/\/doi.org\/10.1007\/3-540-39205-X_101"},{"key":"72_CR18","first-page":"2579","volume":"9","author":"LVD Maaten","year":"2008","unstructured":"Maaten, L.V.D., Hinton, G.: Visualizing high-dimensional data using t-SNE. J. Mach. Learn. Res. 9, 2579\u20132605 (2008)","journal-title":"J. Mach. Learn. Res."},{"key":"72_CR19","unstructured":"Wang, Y., Witten, I.H.: Induction of model trees for predicting continuous classes. In: Proceedings European Conferenve on Machine Learning, Prague, pp. 128\u2013137 (1997)"},{"key":"72_CR20","volume-title":"Data Mining: Practical Machine Learning Tools and Techniques","author":"IH Witten","year":"2005","unstructured":"Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, San Francisco (2005)"},{"key":"72_CR21","doi-asserted-by":"crossref","unstructured":"Vald\u00e9s, J.J., Cheung, C., Wang, W.: Evolutionary computation methods for helicopter loads estimation. In: Proceedings of the 2011 IEEE Congress on Evolutionary Computation, New Orleans, USA, 5\u201311 June 2011 (2011)","DOI":"10.1109\/CEC.2011.5949805"},{"key":"72_CR22","doi-asserted-by":"crossref","unstructured":"Vald\u00e9s, J.J., Cheung, C., Li, M.: Towards conservative helicopter loads prediction using computational intelligence techniques. In: Proceedings of the 2012 IEEE World Congress on Computational Intelligence, International Convention Centre, Brisbane, Australia, 10\u201315 June 2012, pp. 1853\u20131860 (2012)","DOI":"10.1109\/IJCNN.2012.6252624"},{"key":"72_CR23","doi-asserted-by":"crossref","unstructured":"Vald\u00e9s, J.J., Cheung, C., Li, M.: Sensor dynamics in high dimensional phase spaces via nonlinear transformations: application to helicopter loads monitoring. In: 2014 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2014), Orlando, 9\u201312 December 2014 (2014)","DOI":"10.1109\/CIDM.2014.7008691"}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2019: Workshop and Special Sessions"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-30493-5_72","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T16:55:31Z","timestamp":1710348931000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-30493-5_72"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030304928","9783030304935"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-30493-5_72","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"9 September 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Munich","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 September 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2019\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}