<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Nowadays, being in digital era the data generated by various applications are increasing drastically both row-wise and column wise; this creates a bottleneck for analytics and also increases the burden of machine learning algorithms that work for pattern recognition. This cause of dimensionality can be handled through reduction techniques. The Dimensionality Reduction (DR) can be handled in two ways namely Feature Selection (FS) and Feature Extraction (FE). This paper focuses on a survey of feature selection methods, from this extensive survey we can conclude that most of the FS methods use static data. However, after the emergence of IoT and web-based applications, the data are generated dynamically and grow in a fast rate, so it is likely to have noisy data, it also hinders the performance of the algorithm. With the increase in the size of the data set, the scalability of the FS methods becomes jeopardized. So the existing DR algorithms do not address the issues with the dynamic data. Using FS methods not only reduces the burden of the data but also avoids overfitting of the model.</p></abstract>

Nowadays, being in digital era the data generated by various applications are increasing drastically both row-wise and column wise; this creates a bottleneck for analytics and also increases the burden of machine learning algorithms that work for pattern recognition. This cause of dimensionality can be handled through reduction techniques. The Dimensionality Reduction (DR) can be handled in two ways namely Feature Selection (FS) and Feature Extraction (FE). This paper focuses on a survey of feature selection methods, from this extensive survey we can conclude that most of the FS methods use static data. However, after the emergence of IoT and web-based applications, the data are generated dynamically and grow in a fast rate, so it is likely to have noisy data, it also hinders the performance of the algorithm. With the increase in the size of the data set, the scalability of the FS methods becomes jeopardized. So the existing DR algorithms do not address the issues with the dynamic data. Using FS methods not only reduces the burden of the data but also avoids overfitting of the model.

A Review of Feature Selection and Its Methods

Cybernetics and Information Technologies

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

{"article-title":"A Review of Feature Selection and Its Methods"}

Nowadays, being in digital era the data generated by various applications are increasing drastically both row-wise and column wise; this creates a bottleneck...

A Review of Feature Selection and Its Methods

Data publikacji: 29 mar 2019

Zakres stron: 3 - 26

Otrzymano: 02 lut 2018

Przyjęty: 07 lut 2019

DOI: https://doi.org/10.2478/cait-2019-0001

Słowa kluczowe
Dimensionality Reduction (DR), Feature Selection (FS), Feature Extraction (FE)

© 2019 B. Venkatesh et al., published by Sciendo

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

A Review of Feature Selection and Its Methods

Data publikacji: 29 mar 2019

Zakres stron: 3 - 26

Otrzymano: 02 lut 2018

Przyjęty: 07 lut 2019

DOI: https://doi.org/10.2478/cait-2019-0001

Słowa kluczoweDimensionality Reduction (DR), Feature Selection (FS), Feature Extraction (FE)

© 2019 B. Venkatesh et al., published by Sciendo

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

Słowa kluczowe
Dimensionality Reduction (DR), Feature Selection (FS), Feature Extraction (FE)