Zobrazeno 1 - 10
of 37
pro vyhledávání: '"J. Ariel Carrasco-Ochoa"'
Publikováno v:
Artificial Intelligence Review. 55:2821-2846
Feature Selection for mixed data is an active research area with many applications in practical problems where numerical and non-numerical features describe the objects of study. This paper provides the first comprehensive and structured revision of
Publikováno v:
Pattern Recognition Letters. 138:321-328
Spectral analysis and Information-theory are two powerful and successful frameworks for feature selection in supervised classification problems. However, most of the methods developed under these frameworks have been introduced for handling exclusive
Autor:
J. Arturo Olvera-López, Manuel S. Lazo-Cortés, Vladimir Rodríguez-Diez, J. Ariel Carrasco-Ochoa, José Fco. Martínez-Trinidad
Publikováno v:
Pattern Recognition Letters. 138:177-184
This paper deals with the problem of computing the shortest reducts of a decision system. The shortest reducts are useful for attribute reduction in classification problems and data size reduction. Unfortunately, finding all the shortest reducts is a
Publikováno v:
Artificial Intelligence Review. 53:907-948
In recent years, unsupervised feature selection methods have raised considerable interest in many research areas; this is mainly due to their ability to identify and select relevant features without needing class label information. In this paper, we
Publikováno v:
Journal of Intelligent & Fuzzy Systems. 34:2923-2934
Publikováno v:
Pattern Recognition. 72:314-326
Most of the current unsupervised feature selection methods are designed to process only numerical datasets. Therefore, in practical problems, where the objects under study are described through both numerical and non-numerical features (mixed dataset
Autor:
Sudeep Sarkar, José Fco. Martínez-Trinidad, Joaquin Salas, J. Ariel Carrasco-Ochoa, J. Arturo Olvera-López
Publikováno v:
Pattern Recognition Letters. 142:1-2
Publikováno v:
Neurocomputing. 214:866-880
Feature selection is a common task in areas such as Pattern Recognition, Data Mining, and Machine Learning since it can help to improve prediction quality, reduce computation time and build more understandable models. Although feature selection for s
Publikováno v:
Expert Systems with Applications. 162:113745
Unsupervised Feature Selection (UFS) has aroused great interest in the last years because of its practical significance and application on a large variety of problems in expert and intelligent systems where unlabeled data appear. Specifically, Unsupe
Publikováno v:
Advances in Soft Computing ISBN: 9783030044909
MICAI (1)
MICAI (1)
Unsupervised Feature Selection methods have raised considerable interest in the scientific community due to their capability of identifying and selecting relevant features in unlabeled data. In this paper, we evaluate and compare seven of the most wi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::269622f138ab9b47c68091f017f05dc4
https://doi.org/10.1007/978-3-030-04491-6_16
https://doi.org/10.1007/978-3-030-04491-6_16