Feature Selection for Classification: A Review

  title={Feature Selection for Classification: A Review},
  author={Jiliang Tang and Salem Alelyani and Huan Liu},
  booktitle={Data Classification: Algorithms and Applications},
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… 

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.

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
  • 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.



A review of feature selection techniques in bioinformatics

A basic taxonomy of feature selection techniques is provided, providing their use, variety and potential in a number of both common as well as upcoming bioinformatics applications.

The Effect of the Characteristics of the Dataset on the Selection Stability

This work conducts an extensive experimental study using verity of data sets and different well-known feature selection algorithms in order to study the behavior of these algorithms in terms of the stability.

Penalized feature selection and classification in bioinformatics

This article provides a review of several recently developed penalized feature selection and classification techniques--which belong to the family of embedded feature selection methods--for bioinformatics studies with high-dimensional input.

Online Feature Selection and Its Applications

This article investigates the problem of online feature selection (OFS) in which an online learner is only allowed to maintain a classifier involved only a small and fixed number of features, and presents novel algorithms to solve each of the two problems.

Feature Selection for Knowledge Discovery and Data Mining

  • Huan LiuH. Motoda
  • Computer Science
    The Springer International Series in Engineering and Computer Science
  • 1998
Feature Selection for Knowledge Discovery and Data Mining offers an overview of the methods developed since the 1970's and provides a general framework in order to examine these methods and categorize them and suggests guidelines for how to use different methods under various circumstances.

An Introduction to Variable and Feature Selection

The contributions of this special issue cover a wide range of aspects of variable selection: providing a better definition of the objective function, feature construction, feature ranking, multivariate feature selection, efficient search methods, and feature validity assessment methods.

Feature Selection for Classification

Filter versus wrapper gene selection approaches in DNA microarray domains

Feature Selection for Machine Learning: Comparing a Correlation-Based Filter Approach to the Wrapper

A new fllter approach to feature selection that uses a correlation based heuristic to evaluate the worth of feature subsets when applied as a data preprocessing step for two common machine learning algorithms.

A Practical Approach to Feature Selection