<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Feature selection technique has been a very active research topic that addresses the problem of reducing the dimensionality. Whereas, datasets are continuously growing over time both in samples and features number. As a result, handling both irrelevant and redundant features has become a real challenge. In this paper we propose a new straightforward framework which combines the horizontal and vertical distributed feature selection technique, called Horizo-Vertical Distributed Feature Selection approach (HVDFS), aimed at achieving good performances as well as reducing the number of features. The effectiveness of our approach is demonstrated on three well-known datasets compared to the centralized and the previous distributed approach, using four well-known classifiers.</p></abstract>

Feature selection technique has been a very active research topic that addresses the problem of reducing the dimensionality. Whereas, datasets are continuously growing over time both in samples and features number. As a result, handling both irrelevant and redundant features has become a real challenge. In this paper we propose a new straightforward framework which combines the horizontal and vertical distributed feature selection technique, called Horizo-Vertical Distributed Feature Selection approach (HVDFS), aimed at achieving good performances as well as reducing the number of features. The effectiveness of our approach is demonstrated on three well-known datasets compared to the centralized and the previous distributed approach, using four well-known classifiers.

A New Horizo-Vertical Distributed Feature Selection Approach

Mohammed V University in Rabat, IT Center

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"A New Horizo-Vertical Distributed Feature Selection Approach"}

Feature selection technique has been a very active research topic that addresses the problem of reducing the dimensionality. Whereas, datasets are continuously...

A New Horizo-Vertical Distributed Feature Selection Approach

Online veröffentlicht: 14. Dez. 2018

Seitenbereich: 15 - 28

Eingereicht: 05. Apr. 2018

Akzeptiert: 08. Sept. 2018

DOI: https://doi.org/10.2478/cait-2018-0045

Schlüsselwörter
Feature selection, distributed approach, dimensionality reduction

© 2018 B. Zerhari et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

A New Horizo-Vertical Distributed Feature Selection Approach

Online veröffentlicht: 14. Dez. 2018

Seitenbereich: 15 - 28

Eingereicht: 05. Apr. 2018

Akzeptiert: 08. Sept. 2018

DOI: https://doi.org/10.2478/cait-2018-0045

SchlüsselwörterFeature selection, distributed approach, dimensionality reduction

© 2018 B. Zerhari et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
Feature selection, distributed approach, dimensionality reduction