Feature Selection for Classification: A Review

@inproceedings{Tang2014FeatureSF,
  title={Feature Selection for Classification: A Review},
  author={Jiliang Tang and Salem Alelyani and Huan Liu},
  booktitle={Data Classification: Algorithms and Applications},
  year={2014}
}
Nowadays, the growth of the high-throughput technologies has resulted in exponential growth in the harvested data with respect to both dimensionality and sample size. The trend of this growth of the UCI machine learning repository is shown in Figure 1. Efficient and effective management of these data becomes increasing challenging. Traditionally manual management of these datasets to be impractical. Therefore, data mining and machine learning techniques were developed to automatically discover… 
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928 Citations
Highly Influential Citations
45
Background Citations
272
Methods Citations
249
Results Citations
4

928 Citations

Feature selection in machine learning: A new perspective

Introduction to Feature Selection

In this chapter, necessary preliminaries of feature selection are discussed, which lets us select only relevant data that the authors can use on behalf of the entire dataset.

A Review on Dimensionality Reduction Techniques

This paper analyses some existing popular feature selection and feature extraction techniques and addresses benefits and challenges of these algorithms which would be beneficial for beginners.

A Novel Feature Selection Method Based on Clustering

A feature selection method based on the mean shift clustering algorithm and the Pearson correlation coefficient is proposed to contribute to solving some of the challenges in the data analytics systems, of real-time execution.

Application of feature selection methods for automated clustering analysis: a review on synthetic datasets

The proposed approach allows the SOM to converge before analysing the input relevance, unlike the WSOM that aims to apply weighting to the inputs during the training which distorts the SOM’s cost function, resulting in multiple local minimums meaning the SOM does not consistently converge to the same state.

Feature Selection using Genetic Programming

This paper investigates the ability of Genetic Programming (GP), an evolutionary algorithm searching strategy capable of automatically finding solutions in complex and large search spaces, to perform feature selection and shows that not only does GP select a smaller set of features from the original features, classifiers using GP selected features achieve a better classification performance than using all the original Features.

A New Intelligent Hybrid Feature Selection Method

  • M. A. AleniziH. Y. Mansour
  • Computer Science
    2018 IEEE/ACS 15th International Conference on Computer Systems and Applications (AICCSA)
  • 2018
A new hybrid feature selection method is introduced and evaluated against ten datasets form UCI repository and experimental results show that the classifier adopted to the experiment has achieved better classification accuracy when compared with the other version that used a single feature selection methods.

Big Data classification: techniques and tools

Feature selection techniques in the context of big data: taxonomy and analysis

A comprehensive review of the latest FS approaches in the context of big data along with a structured taxonomy, which categorizes the existing methods based on their nature, search strategy, evaluation process, and feature structure and highlights the research issues and open challenges related to FS.

Evaluating Feature Selection Robustness on High-Dimensional Data

  • B. Pes
  • Computer Science
    HAIS
  • 2018
The robustness of some state-of-the-art selection methods, for different levels of data perturbation and different cardinalities of the selected feature subsets are analyzed.
...

References

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