{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T10:19:34Z","timestamp":1743070774506,"version":"3.40.3"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319681948"},{"type":"electronic","value":"9783319681955"}],"license":[{"start":{"date-parts":[[2017,10,13]],"date-time":"2017-10-13T00:00:00Z","timestamp":1507852800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"DOI":"10.1007\/978-3-319-68195-5_121","type":"book-chapter","created":{"date-parts":[[2017,10,12]],"date-time":"2017-10-12T01:14:26Z","timestamp":1507770866000},"page":"1091-1101","source":"Crossref","is-referenced-by-count":1,"title":["Impact of Feature Selection on Average Ranking Method via Metalearning"],"prefix":"10.1007","author":[{"given":"Salisu Mamman","family":"Abdulrahman","sequence":"first","affiliation":[]},{"given":"Miguel Viana","family":"Cachada","sequence":"additional","affiliation":[]},{"given":"Pavel","family":"Brazdil","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2017,10,13]]},"reference":[{"key":"121_CR1","unstructured":"Abdulrahman, S.M., Brazdil, P.: Measures for combining accuracy and time for meta-learning. In: Meta-Learning and Algorithm Selection Workshop at ECAI 2014, pp. 49\u201350 (2014)"},{"key":"121_CR2","first-page":"37","volume":"1","author":"SM Abdulrahman","year":"2017","unstructured":"Abdulrahman, S.M., Brazdil, P., van Rijn, J.N., Vanschoren, J.: Speeding up algorithm selection using average ranking and active testing by introducing runtime. Mach. Learn. 1, 37\u201366 (2017). Special Issue on Metalearning and Algorithm Selection","journal-title":"Mach. Learn."},{"key":"121_CR3","unstructured":"Bergstra, J.S., Bardenet, R., Bengio, Y., K\u00e9gl, B.: Algorithms for hyper-parameter optimization. In: Advances in Neural Information Processing Systems, pp. 2546\u20132554 (2011)"},{"key":"121_CR4","doi-asserted-by":"crossref","unstructured":"Brazdil, P., Giraud-Carrier, C., Soares, C., Vilalta, R.: Metalearning: Applications to Data Mining. Springer Science & Business Media (2008)","DOI":"10.1007\/978-3-540-73263-1"},{"issue":"3","key":"121_CR5","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1023\/A:1021713901879","volume":"50","author":"P Brazdil","year":"2003","unstructured":"Brazdil, P., Soares, C., da Costa, J.P.: Ranking learning algorithms: using IBL and meta-learning on accuracy and time results. Mach. Learn. 50(3), 251\u2013277 (2003)","journal-title":"Mach. Learn."},{"issue":"1","key":"121_CR6","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.neuroimage.2011.11.066","volume":"60","author":"C Chu","year":"2012","unstructured":"Chu, C., Hsu, A.L., Chou, K.H., Bandettini, P., Lin, C., Initiative, A.D.N.: Does feature selection improve classification accuracy? Impact of sample size and feature selection on classification using anatomical magnetic resonance images. Neuroimage 60(1), 59\u201370 (2012)","journal-title":"Neuroimage"},{"key":"121_CR7","unstructured":"Cuingnet, R., Chupin, M., Benali, H., Colliot, O.: Spatial and anatomical regularization of SVM for brain image analysis. In: Advances in Neural Information Processing Systems, pp. 460\u2013468 (2010)"},{"key":"121_CR8","first-page":"1","volume":"7","author":"J Dem\u0161ar","year":"2006","unstructured":"Dem\u0161ar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1\u201330 (2006)","journal-title":"J. Mach. Learn. Res."},{"key":"121_CR9","unstructured":"Eggensperger, K., Feurer, M., Hutter, F., Bergstra, J., Snoek, J., Hoos, H., Leyton-Brown, K.: Towards an empirical foundation for assessing Bayesian optimization of hyperparameters. In: NIPS workshop on Bayesian Optimization in Theory and Practice, pp. 1\u20135 (2013)"},{"key":"121_CR10","unstructured":"Feurer, M., Klein, A., Eggensperger, K., Springenberg, J., Blum, M., Hutter, F.: Efficient and robust automated machine learning. In: Advances in Neural Information Processing Systems, pp. 2962\u20132970 (2015)"},{"key":"121_CR11","doi-asserted-by":"crossref","unstructured":"Gansterer, W.N., Janecek, A.G., Neumayer, R.: Spam filtering based on latent semantic indexing. In: Survey of Text Mining II, pp. 165\u2013183. Springer (2008)","DOI":"10.1007\/978-1-84800-046-9_9"},{"issue":"1","key":"121_CR12","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.neucom.2011.07.005","volume":"75","author":"TA Gomes","year":"2012","unstructured":"Gomes, T.A., Prud\u0142ncio, R.B., Soares, C., Rossi, A.L., Carvalho, A.: Combining meta-learning and search techniques to select parameters for support vector machines. Neurocomputing 75(1), 3\u201313 (2012)","journal-title":"Neurocomputing"},{"key":"121_CR13","first-page":"1157","volume":"3","author":"I Guyon","year":"2003","unstructured":"Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. JMLR 3, 1157\u20131182 (2003)","journal-title":"J. Mach. Learn. Res. JMLR"},{"issue":"1","key":"121_CR14","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1145\/1656274.1656278","volume":"11","author":"M Hall","year":"2009","unstructured":"Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. ACM SIGKDD Explor. Newslett. 11(1), 10\u201318 (2009)","journal-title":"ACM SIGKDD Explor. Newslett."},{"key":"121_CR15","unstructured":"Hall, M.A.: Correlation-based feature selection for machine learning. Ph.D. thesis, The University of Waikato (1999)"},{"key":"121_CR16","doi-asserted-by":"crossref","unstructured":"Hutter, F., Hoos, H.H., Leyton-Brown, K.: Sequential model-based optimization for general algorithm configuration. In: International Conference on Learning and Intelligent Optimization, pp. 507\u2013523 (2011)","DOI":"10.1007\/978-3-642-25566-3_40"},{"issue":"1","key":"121_CR17","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/S0004-3702(97)00043-X","volume":"97","author":"R Kohavi","year":"1997","unstructured":"Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artif. Intell. 97(1), 273\u2013324 (1997)","journal-title":"Artif. Intell."},{"key":"121_CR18","first-page":"1","volume":"17","author":"L Kotthoff","year":"2016","unstructured":"Kotthoff, L., Thornton, C., Hoos, H.H., Hutter, F., Leyton-Brown, K.: Auto-weka 2.0: automatic model selection and hyperparameter optimization in weka. J. Mach. Learn. Res. 17, 1\u20135 (2016)","journal-title":"J. Mach. Learn. Res."},{"key":"121_CR19","doi-asserted-by":"crossref","unstructured":"Leite, R., Brazdil, P.: Active testing strategy to predict the best classification algorithm via sampling and metalearning. In: ECAI, pp. 309\u2013314 (2010)","DOI":"10.1007\/978-3-642-05177-7_8"},{"key":"121_CR20","doi-asserted-by":"crossref","unstructured":"Leite, R., Brazdil, P., Vanschoren, J.: Selecting classification algorithms with active testing. In: Machine Learning and Data Mining in Pattern Recognition, pp. 117\u2013131. Springer (2012)","DOI":"10.1007\/978-3-642-31537-4_10"},{"key":"121_CR21","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1002\/wics.111","volume":"2","author":"S Lin","year":"2010","unstructured":"Lin, S.: Rank aggregation methods. WIREs Comput. Statistics 2, 555\u2013570 (2010)","journal-title":"WIREs Comput. Statistics"},{"key":"121_CR22","unstructured":"Maclaurin, D., Duvenaud, D., Adams, R.P.: Gradient-based hyperparameter optimization through reversible learning. In: Proceedings of the 32nd International Conference on Machine Learning (2015)"},{"key":"121_CR23","doi-asserted-by":"crossref","unstructured":"de Miranda, P.B., Prud\u00eancio, R.B., de Carvalho, A.C.P., Soares, C.: Combining a multi-objective optimization approach with meta-learning for SVM parameter selection. In: Systems, Man, and Cybernetics (SMC), pp. 2909\u20132914. IEEE (2012)","DOI":"10.1109\/ICSMC.2012.6378235"},{"key":"121_CR24","unstructured":"Molina, L.C., Belanche, L., Nebot, A.: Feature selection algorithms: a survey and experimental evaluation. In: IEEE International Conference on Proceedings, pp. 306\u2013313. IEEE (2003)"},{"key":"121_CR25","volume-title":"Distribution-free Tests","author":"HR Neave","year":"1988","unstructured":"Neave, H.R., Worthington, P.L.: Distribution-free Tests. Unwin Hyman, London (1988)"},{"key":"121_CR26","unstructured":"Pfahringer, B., Bensusan, H., Giraud-Carrier, C.: Tell me who can learn you and I can tell you who you are: landmarking various learning algorithms. In: Proceedings of the 17th International Conference on Machine Learning, pp. 743\u2013750 (2000)"},{"issue":"3","key":"121_CR27","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1007\/s10994-012-5286-7","volume":"87","author":"M Reif","year":"2012","unstructured":"Reif, M., Shafait, F., Dengel, A.: Meta-learning for evolutionary parameter optimization of classifiers. Mach. Learn. 87(3), 357\u2013380 (2012)","journal-title":"Mach. Learn."},{"key":"121_CR28","doi-asserted-by":"crossref","unstructured":"van Rijn, J.N., Abdulrahman, S.M., Brazdil, P., Vanschoren, J.: Fast algorithm selection using learning curves. In: Advances in Intelligent Data Analysis XIV. Springer (2015)","DOI":"10.1007\/978-3-319-24465-5_26"},{"issue":"19","key":"121_CR29","doi-asserted-by":"crossref","first-page":"2507","DOI":"10.1093\/bioinformatics\/btm344","volume":"23","author":"Y Saeys","year":"2007","unstructured":"Saeys, Y., Inza, I., Larraaga, P.: A review of feature selection techniques in bioinformatics. Bioinformatics 23(19), 2507\u20132517 (2007)","journal-title":"Bioinformatics"},{"issue":"1","key":"121_CR30","first-page":"6:1","volume":"41","author":"KA Smith-Miles","year":"2008","unstructured":"Smith-Miles, K.A.: Cross-disciplinary perspectives on meta-learning for algorithm selection. ACM Comput. Surv. (CSUR) 41(1), 6:1\u20136:25 (2008)","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"121_CR31","unstructured":"Snoek, J., Larochelle, H., Adams, R.P.: Practical Bayesian optimization of machine learning algorithms. In: Advances in Neural Information Processing Systems, pp. 2951\u20132959 (2012)"},{"issue":"2","key":"121_CR32","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1145\/2641190.2641198","volume":"15","author":"J Vanschoren","year":"2013","unstructured":"Vanschoren, J., Van Rijn, J.N., Bischl, B., Torgo, L.: OpenML: networked science in machine learning. ACM SIGKDD Explor. Newslett. 15(2), 49\u201360 (2013)","journal-title":"ACM SIGKDD Explor. Newslett."}],"container-title":["Lecture Notes in Computational Vision and Biomechanics","VipIMAGE 2017"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-68195-5_121","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,10,4]],"date-time":"2019-10-04T10:24:02Z","timestamp":1570184642000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-68195-5_121"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,10,13]]},"ISBN":["9783319681948","9783319681955"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-68195-5_121","relation":{},"ISSN":["2212-9391","2212-9413"],"issn-type":[{"type":"print","value":"2212-9391"},{"type":"electronic","value":"2212-9413"}],"subject":[],"published":{"date-parts":[[2017,10,13]]}}}