0368741 20240903170624.120111208235959.9 000293207900007 83455221244 Improving feature selection process resistance to failures caused by curse-of-dimensionality effects 25 s. cav_un_epca*02971630023-5954Kybernetika473 (2011)401-425Ústav teorie informace a automatizace AV ČR, v. v. i. feature selection curse of dimensionality over-fitting stability machine learning dimensionality reduction cav_un_auth*0101197 Somol Petr Rozpoznávání obrazu Department of Pattern Recognition RO RO UTIA-B Department of Pattern Recognition Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101091 Grim Jiří Rozpoznávání obrazu Department of Pattern Recognition RO RO UTIA-B Department of Pattern Recognition Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101171 Novovičová Jana Rozpoznávání obrazu Department of Pattern Recognition RO RO UTIA-B Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0021092 Pudil P. CZ http://library.utia.cas.cz/separaty/2011/RO/somol-0368741.pdf 1M0572 GA MŠk cav_un_auth*0001814 2C06019 GA MŠk CZ cav_un_auth*0216518 GA102/08/0593 GA ČR cav_un_auth*0239567 CEZ:AV0Z10750506 The purpose of feature selection in machine learning is at least two-fold – saving measurement acquisition costs and reducing the negative effects of the curse of dimensionality with the aim to improve the accuracy of the models and the classification rate of classifiers with respect to previously unknown data. Yet it has been shown recently that the process of feature selection itself can be negatively affected by the very same curse of dimensionality – feature selection methods may easily over-fit or perform unstably. Such an outcome is unlikely to generalize well and the resulting recognition system may fail to deliver the expectable performance. In many tasks, it is therefore crucial to employ additional mechanisms of making the feature selection process more stable and resistant the curse of dimensionality effects. In this paper we discuss three different approaches to reducing this problem. 2012 IN 4 4 A O 4a 4o 20231122134815.9 http://hdl.handle.net/11104/0203004 COMPUTERSCIENCECYBERNETICS 0.473 0.033 9.5 0.0016 0.277 403 61 21 0.307 20.45 0.61 Q2 23.915 17.500 Q4 Q3 2011 Soubory v repozitáři: somol-0368741.pdf, 0368741.pdf 83455221244 SCOPUS 000293207900007 WOS cav_un_epca*0297163 Kybernetika 0023-5954 Roč. 47 č. 3 2011 401 425 Ústav teorie informace a automatizace AV ČR, v. v. i.