{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T17:42:27Z","timestamp":1754156547005,"version":"3.41.2"},"reference-count":30,"publisher":"Emerald","issue":"4","license":[{"start":{"date-parts":[[2014,11,4]],"date-time":"2014-11-04T00:00:00Z","timestamp":1415059200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2014,11,4]]},"abstract":"<jats:sec>\n               <jats:title content-type=\"abstract-heading\">Purpose<\/jats:title>\n               <jats:p> \u2013 Many applications in intelligent transportation demand accurate categorization of vehicles. The purpose of this paper is to propose a working image-based vehicle classification system. The first component vehicle detection is implemented by applying Dalal and Triggs's histograms of oriented gradients features and linear support vector machine (SVM) classifier. The second component vehicle classification, which is the emphasis of this paper, is accomplished by an improved stacked generalization. As an effective ensemble learning strategy, stacked generalization has been proposed to combine multiple models using the concept of a meta-learner. However, it was found that the well-known meta-learning scheme multi-response linear regression (MLR) for stacked generalization performs poorly on the vehicle classification. <\/jats:p>\n            <\/jats:sec>\n            <jats:sec>\n               <jats:title content-type=\"abstract-heading\">Design\/methodology\/approach<\/jats:title>\n               <jats:p> \u2013 A new meta-learner is then proposed based on kernel principal component regression (KPCR). The stacked generalization scheme consists of a heterogeneous classifier ensemble with seven base classifiers, i.e. linear discriminant classifier, fuzzy <jats:italic>k<\/jats:italic>-nearest neighbor, logistic regression, Parzen classifier, Gaussian mixture model, multiple layer perceptron and SVM. <\/jats:p>\n            <\/jats:sec>\n            <jats:sec>\n               <jats:title content-type=\"abstract-heading\">Findings<\/jats:title>\n               <jats:p> \u2013 Experimental results using more than 2,500 images from four types of vehicles (bus, light truck, car and van) demonstrated the effectiveness of the proposed approach. The improved stacked generalization produced consistently better results when compared to any of the single base classifier used and four other beta learning algorithms, including MLR, majority voting, logistic regression and decision template. <\/jats:p>\n            <\/jats:sec>\n            <jats:sec>\n               <jats:title content-type=\"abstract-heading\">Originality\/value<\/jats:title>\n               <jats:p> \u2013 With the seven base classifiers, the KPCR-based stacking offers a performance of 96 percent accuracy and 95 percent <jats:italic>\u03ba<\/jats:italic> coefficient, thus exhibiting promising potentials for real-world applications.<\/jats:p>\n            <\/jats:sec>","DOI":"10.1108\/ijicc-06-2013-0030","type":"journal-article","created":{"date-parts":[[2014,11,10]],"date-time":"2014-11-10T12:53:11Z","timestamp":1415623991000},"page":"415-435","source":"Crossref","is-referenced-by-count":3,"title":["Vehicle identification by improved stacking via kernel principal component regression"],"prefix":"10.1108","volume":"7","author":[{"given":"Bailing","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Pan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","reference":[{"key":"key2020122722563508000_b1","doi-asserted-by":"crossref","unstructured":"Ben-David, A.\n                (2006), \u201cComparison of classification accuracy using Cohen's Weighted Kappa\u201d, Expert Systems with Applications, Vol. 34 No. 2, pp. 825-832.","DOI":"10.1016\/j.eswa.2006.10.022"},{"key":"key2020122722563508000_b2","doi-asserted-by":"crossref","unstructured":"Bishop, C.\n                (1996), Neural Networks for Pattern Recognition, Oxford University Press, New York, NY.","DOI":"10.1201\/9781420050646.ptb6"},{"key":"key2020122722563508000_b3","doi-asserted-by":"crossref","unstructured":"Cortes, C.\n                and \n                  Vapnik, V.\n                (1995), \u201cSupport vector networks\u201d, Machine Learning, Vol. 20 No. 3, pp. 273-297.","DOI":"10.1007\/BF00994018"},{"key":"key2020122722563508000_b4","doi-asserted-by":"crossref","unstructured":"Cretu, A.-M.\n                and \n                  Payeur, P.\n                (2011), \u201cBiologically-inspired visual attention features for a vehicle classification task\u201d, Int. Journal Smart Sensing and Intelligent Systems, Vol. 4 No. 3, pp. 402-423.","DOI":"10.21307\/ijssis-2017-447"},{"key":"key2020122722563508000_b5","doi-asserted-by":"crossref","unstructured":"Dalal, N.\n                and \n                  Triggs, B.\n                (2005), \u201cHistograms of oriented gradients for human detection\u201d, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), San Diego, CA, pp. 886-893.","DOI":"10.1109\/CVPR.2005.177"},{"key":"key2020122722563508000_b6","unstructured":"Duda, R.O.\n               , \n                  Hart, P.E.\n                and \n                  Stork, D.G.\n                (2001), Pattern Classification, 2nd ed., John Wiley and Sons, New York, NY."},{"key":"key2020122722563508000_b7","doi-asserted-by":"crossref","unstructured":"Gupte, S.\n               , \n                  Masoud, O.\n               , \n                  Martin, R.\n                and \n                  Papanikolopoulos, N.\n                (2002), \u201cDetection and classification of vehicles\u201d, IEEE Transactions on Intelligent Transportation Systems, Vol. 3 No. 1, pp. 37-47.","DOI":"10.1109\/6979.994794"},{"key":"key2020122722563508000_b8","doi-asserted-by":"crossref","unstructured":"Hsieh, J.\n               , \n                  Yu, S.\n               , \n                  Chen, Y.\n                and \n                  Hu, W.\n                (2006), \u201cAutomatic traffic surveillance system for vehicle tracking and classification\u201d, IEEE Transactions on Intelligent Transportation Systems, Vol. 7 No. 2, pp. 175-187.","DOI":"10.1109\/TITS.2006.874722"},{"key":"key2020122722563508000_b9","doi-asserted-by":"crossref","unstructured":"Hsu, C.\n                and \n                  Lin, C.\n                (2002), \u201cA comparison on methods for multi-class support vector machines\u201d, IEEE Transactions on Neural Networks, Vol. 13 No. 2, pp. 415-425.","DOI":"10.1109\/72.991427"},{"key":"key2020122722563508000_b10","doi-asserted-by":"crossref","unstructured":"Keller, J.\n               , \n                  Gray, R.\n                and \n                  Givens, J.\n                (1985), \u201cA fuzzy k-nearest neighbor algorithm\u201d, IEEE Trans. Systems Man Cybernet, Vol. 15 No. 4, pp. 580-585.","DOI":"10.1109\/TSMC.1985.6313426"},{"key":"key2020122722563508000_b12","doi-asserted-by":"crossref","unstructured":"Kuncheva, L.\n                (2001), \u201cDecision templates for multiple classifier fusion: an experimental comparison\u201d, Pattern Recognition, Vol. 34 No. 2, pp. 299-314.","DOI":"10.1016\/S0031-3203(99)00223-X"},{"key":"key2020122722563508000_b13","doi-asserted-by":"crossref","unstructured":"Kuncheva, L.\n                (2002), \u201cA theoretical study on six classifier fusion strategies\u201d, IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 24 No. 2, pp. 281-286.","DOI":"10.1109\/34.982906"},{"key":"key2020122722563508000_b11","doi-asserted-by":"crossref","unstructured":"Kuncheva, L.\n                (2004), Combining Pattern Classifiers: Methods and Algorithms, Wiley-Interscience, Hoboken, NJ.","DOI":"10.1002\/0471660264"},{"key":"key2020122722563508000_b14","doi-asserted-by":"crossref","unstructured":"Lam, L.\n                and \n                  Suen, C.\n                (1997), \u201cApplication of majority voting to pattern recognition: an analysis of its behavior and performance\u201d, IEEE Transactions on Systems, Man, and Cybernetics -Part A: Systems and Human, Vol. 27 No. 5, pp. 553-568.","DOI":"10.1109\/3468.618255"},{"key":"key2020122722563508000_b16","doi-asserted-by":"crossref","unstructured":"McLachlan, G.J.\n                and \n                  Peel, D.\n                (2000), Finite Mixture Models, Wiley, New York, NY.","DOI":"10.1002\/0471721182"},{"key":"key2020122722563508000_b15","doi-asserted-by":"crossref","unstructured":"Maji, S.\n               , \n                  Berg, A.\n                and \n                  Malik, J.\n                (2008), \u201cClassification using intersection Kernel support vector machines is efficient\u201d, Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR08), Anchorage, Alaska, pp. 1-8.","DOI":"10.1109\/CVPR.2008.4587630"},{"key":"key2020122722563508000_b17","doi-asserted-by":"crossref","unstructured":"Mussa, R.\n               , \n                  Kwigizile, V.\n                and \n                  Selekwa, M.\n                (2006), \u201cProbabilistic neural networks application for vehicle classification\u201d, Journal of Transportation Engineering, Vol. 132 No. 4, pp. 293-302.","DOI":"10.1061\/(ASCE)0733-947X(2006)132:4(293)"},{"key":"key2020122722563508000_b18","doi-asserted-by":"crossref","unstructured":"Negri, P.\n               , \n                  Clady, X.\n               , \n                  Hanif, S.M.\n                and \n                  Prevost, L.\n                (2008), \u201cA cascade of boosted generative and discriminative classifiers for vehicle detection\u201d, EURASIP Journal on Advances in Signal Processing, Vol. 2008, Article ID 782432, 12pp., doi:10.1155\/2008\/782432, available at: http:\/\/asp.eurasipjournals.com\/content\/pdf\/1687-6180-2008-782432.pdf","DOI":"10.1155\/2008\/782432"},{"key":"key2020122722563508000_b19","unstructured":"Rosipal, R.\n                and \n                  Trejo, L.\n                (2002), \u201cKernel partial least squares regression in reproducing kernel hilbert space\u201d, Journal of Machine Learning Research, Vol. 2, pp. 97-123."},{"key":"key2020122722563508000_b20","doi-asserted-by":"crossref","unstructured":"Rosipal, R.\n               , \n                  Girolami, M.\n               , \n                  Trejo, L.J.\n                and \n                  Cichocki, A.\n                (2001), \u201cKernel PCA for feature extraction and de-noising in non-linear regression\u201d, Neural Computing & Applications, Vol. 10, pp. 231-243.","DOI":"10.1007\/s521-001-8051-z"},{"key":"key2020122722563508000_b22","doi-asserted-by":"crossref","unstructured":"Shawe-Taylor, J.\n                and \n                  Cristianini, N.\n                (2004), Kernel Methods for Pattern Analysis, Cambridge University Press.","DOI":"10.1017\/CBO9780511809682"},{"key":"key2020122722563508000_b23","doi-asserted-by":"crossref","unstructured":"Sikora, T.\n                (2001), \u201cThe MPEG-7 visual standard for content description \u2013 an overview\u201d, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 11 No. 6, pp. 696-702.","DOI":"10.1109\/76.927422"},{"key":"key2020122722563508000_b24","doi-asserted-by":"crossref","unstructured":"Sivaraman, S.\n                and \n                  Trivedi, M.\n                (2010), \u201cGeneral active-learning framework for on-road vehicle recognition and tracking\u201d, IEEE Transactions on Intelligent Transportation Systems, Vol. 11 No. 2, pp. 267-276.","DOI":"10.1109\/TITS.2010.2040177"},{"key":"key2020122722563508000_b25","doi-asserted-by":"crossref","unstructured":"Sun, Z.\n               , \n                  Bebis, G.\n                and \n                  Miller, R.\n                (2005), \u201cOn-road vehicle detection using evolutionary Gabor filter optimization\u201d, IEEE Transactions on Intelligent Transportation Systems, Vol. 6 No. 2, pp. 125-137.","DOI":"10.1109\/TITS.2005.848363"},{"key":"key2020122722563508000_b26","doi-asserted-by":"crossref","unstructured":"Ting, K.M.\n                and \n                  Witten, I.H.\n                (1999), \u201cIssues in stacked generalization\u201d, Journal of Artificial Intelligence Research, Vol. 10, pp. 271-289.","DOI":"10.1613\/jair.594"},{"key":"key2020122722563508000_b28","doi-asserted-by":"crossref","unstructured":"Viola, P.\n                and \n                  Jones, M.\n                (2004), \u201cRobust real-time face detection\u201d, International Journal of Computer Vision, Vol. 57 No. 2, pp. 137-154.","DOI":"10.1023\/B:VISI.0000013087.49260.fb"},{"key":"key2020122722563508000_b29","doi-asserted-by":"crossref","unstructured":"Wen, Y.\n               , \n                  Lu, Y.\n               , \n                  Yan, J.\n               , \n                  Zhou, Z.\n               , \n                  Deneen, K.M.\n                and \n                  Shi, P.\n                (2011), \u201cAn algorithm for license plate recognition applied to intelligent transportation system\u201d, IEEE Transactions on Intelligent Transportation Systems, Vol. 12 No. 3, pp. 830-845.","DOI":"10.1109\/TITS.2011.2114346"},{"key":"key2020122722563508000_b30","doi-asserted-by":"crossref","unstructured":"Wolpert, D.H.\n                (1992), \u201cStacked generalization\u201d, Neural Networks, Vol. 5 No. 2, pp. 241-259.","DOI":"10.1016\/S0893-6080(05)80023-1"},{"key":"key2020122722563508000_frd1","doi-asserted-by":"crossref","unstructured":"SchJolkopf, B.\n               , \n                  Mika, S.\n               , \n                  Burges, C.\n               , \n                  Knirsch, P.\n               , \n                  MJuller, K.\n               , \n                  Jatsch, G.\n                and \n                  Smola, A.\n                (1999), \u201cInput space versus feature space in kernel-based methods\u201d, IEEE Transactions on Neural Networks, Vol. 10 No. 5, pp. 1000-1016.","DOI":"10.1109\/72.788641"},{"key":"key2020122722563508000_frd2","doi-asserted-by":"crossref","unstructured":"Todorovski, L.\n                and \n                  Dzeroski, S.\n                (2000), \u201cCombining multiple models with meta decision trees\u201d, Proceedings of the Fourth European Conference on Principles of Data Mining and Knowledge Discovery, Springer, Berlin, pp. 54-64.","DOI":"10.1007\/3-540-45372-5_6"}],"container-title":["International Journal of Intelligent Computing and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/www.emeraldinsight.com\/doi\/full-xml\/10.1108\/IJICC-06-2013-0030","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/IJICC-06-2013-0030\/full\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/IJICC-06-2013-0030\/full\/html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T22:54:21Z","timestamp":1753397661000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.emerald.com\/ijicc\/article\/7\/4\/415-435\/133345"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014,11,4]]},"references-count":30,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2014,11,4]]}},"alternative-id":["10.1108\/IJICC-06-2013-0030"],"URL":"https:\/\/doi.org\/10.1108\/ijicc-06-2013-0030","relation":{},"ISSN":["1756-378X"],"issn-type":[{"type":"print","value":"1756-378X"}],"subject":[],"published":{"date-parts":[[2014,11,4]]}}}