Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Jacek Koronacki"'
Autor:
Jacek Koronacki, Michał Dramiński
Publikováno v:
Journal of Statistical Software; Vol 85 (2018); 1-28
Journal of Statistical Software, Vol 85, Iss 1, Pp 1-28 (2018)
Journal of Statistical Software, Vol 85, Iss 1, Pp 1-28 (2018)
We describe the R package rmcfs that implements an algorithm for ranking features from high dimensional data according to their importance for a given supervised classification task. The ranking is performed prior to addressing the classification tas
Autor:
Jan Komorowski, Jakub Mieczkowski, Marcin Kruczyk, Nicholas Baltzer, Michał Dramiński, Jacek Koronacki
Publikováno v:
Fundamenta Informaticae. 127:273-288
An important step prior to constructing a classifier for a very large data set is feature selection. With many problems it is possible to find a subset of attributes that have the same discriminative power as the full data set. There are many feature
Publikováno v:
Advances in Machine Learning II ISBN: 9783642051784
Advances in Machine Learning II
Advances in Machine Learning II
Applications of machine learning techniques in Life Sciences are the main applications forcing a paradigm shift in the way these techniques are used. Rather than obtaining the best possible supervised classifier, the Life Scientist needs to know whic
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5a67205f033d5dab9f616ebc5d747fe5
https://doi.org/10.1007/978-3-642-05179-1_17
https://doi.org/10.1007/978-3-642-05179-1_17
Publikováno v:
Rough Sets and Current Trends in Computing ISBN: 9783540476931
RSCTC
RSCTC
A new method for estimation of attributes’ importance for supervised classification, based on the random forest approach, is presented. Essentially, an iterative scheme is applied, with each step consisting of several runs of the random forest prog
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::af865a6c2a6651e33f04c5cbbfc48603
https://doi.org/10.1007/11908029_58
https://doi.org/10.1007/11908029_58
Publikováno v:
Principles of Data Mining and Knowledge Discovery ISBN: 9783540664901
PKDD
PKDD
Unsupervised classification of objects involves formation of classes and construction of one or more taxonomies that include those classes. Meaningful classes can be formed in feedback with acquisition of knowledge about each class. We demonstrate ho
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a23a08801ab43419b7aa30f25ea0b35d
https://doi.org/10.1007/978-3-540-48247-5_8
https://doi.org/10.1007/978-3-540-48247-5_8