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17 results on '"Solorio-Fernández, Saúl"'

3. Unsupervised Feature Selection Methodology for Analysis of Bacterial Taxonomy Profiles

Author

Solorio-Fernández, Saúl, Carrasco-Ochoa, J. A., Martínez-Trinidad, José Fco., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Roman-Rangel, Edgar, editor, Kuri-Morales, Ángel Fernando, editor, Martínez-Trinidad, José Francisco, editor, Carrasco-Ochoa, Jesús Ariel, editor, and Olvera-López, José Arturo, editor

Published
2021
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6. A Supervised Filter Feature Selection Method for Mixed Data Based on the Spectral Gap Score

Author

Solorio-Fernández, Saúl, Martínez-Trinidad, José Fco., Carrasco-Ochoa, Jesús Ariel, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Carrasco-Ochoa, Jesús Ariel, editor, Martínez-Trinidad, José Francisco, editor, Olvera-López, José Arturo, editor, and Salas, Joaquín, editor

Published
2019
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8. Ranking Based Unsupervised Feature Selection Methods: An Empirical Comparative Study in High Dimensional Datasets

Author

Solorio-Fernández, Saúl, Carrasco-Ochoa, J. Ariel, Martínez-Trinidad, José Fco., Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Batyrshin, Ildar, editor, Martínez-Villaseñor, María de Lourdes, editor, and Ponce Espinosa, Hiram Eredín, editor

Published
2018
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11. Hybrid Feature Selection Method for Supervised Classification Based on Laplacian Score Ranking

Author

Solorio-Fernández, Saúl, Carrasco-Ochoa, J. Ariel, Martínez-Trinidad, José Fco., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Martínez-Trinidad, José Francisco, editor, and Carrasco-Ochoa, Jesús Ariel, editor

Published
2010
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12. Runtime Prediction of Filter Unsupervised Feature Selection Methods

Author

van der Weij, Teun, Soancatl Aguilar, Venustiano, Solorio-Fernández, Saúl, and Department of Sciences

Abstract

In recent years, the speed and quality of data analysis have been hindered by an increase in data size, an increase in data dimensionality, and the expensive task of data labeling. Much research has been conducted in the field of Unsupervised Feature Selection (UFS) to counteract this hindrance. Specifically, filter UFS methods are popular due to their simplicity and efficiency in counteracting performance problems in unlabeled data analysis. However, this popularity resulted in a great variety of filter UFS methods, each with their own advantages and disadvantages, making it hard to choose an appropriate method for a particular problem. Unfortunately, an inappropriate method choice can lead to a decrease in research or project quality, and it can render data analysis unfeasible due to time constraints. Importantly, terminating a method’s analysis before completion means in most cases that no partial results are obtained either. Previous works on the evaluation of filter UFS methods focused mainly on assessing clustering and classification performance. Although very useful, choosing an appropriate method often requires knowledge about the method’s runtime as well. In this paper, we study the runtimes of six popular filter UFS methods using synthetic and real-world datasets. Runtime prediction models were trained on 114 synthetic datasets and tested on 29 real-world datasets. The models showed good performance on four out of the six methods. Finally, we present general runtime guidelines for each method. To the best of our knowledge, this is the first paper that investigates methods’ runtimes in this fashion.

Published
2022

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