Literature Review on Feature Selection Methods for High-Dimensional Data

@article{Singh2016LiteratureRO,
  title={Literature Review on Feature Selection Methods for High-Dimensional Data},
  author={D. Asir Antony Gnana Singh and S. Appavu alias Balamurugan and Epiphany Jebamalar Leavline},
  journal={International Journal of Computer Applications},
  year={2016},
  volume={136},
  pages={9-17}
}
selection is a process of removing the redundant and the irrelevant features from a dataset to improve the performance of the machine learning algorithms. The feature selection is also known as variable selection or attribute selection. The features are also known as variables or attributes. The machine learning algorithms can be roughly classified into two categories one is supervised learning algorithm and another one is unsupervised learning algorithm. The supervised learning algorithms… Expand
Analysis of Feature Selection Algorithms and a Comparative study on Heterogeneous Classifier for High Dimensional Data survey
TLDR
The application of best feature selection techniques to improve learning algorithm predictive accuracy in microarray dataset and KDD (Knowledge Discovery and Data Mining Tools Conference) Cup 99 dataset with respective classification and feature selection algorithms. Expand
Heterogeneous Ensemble Methods Based On Filter Feature Selection
TLDR
Evaluated ensemble methods based on stacking, voting and multischeme with a framework on the performance measurement of base classifiers and ensemble methods with and without feature selection techniques, it can be concluded that ensemble methods works well with feature selection. Expand
Feature Selection for Small Sample Sets with High Dimensional Data Using Heuristic Hybrid Approach
TLDR
A novel hybrid feature selection technique is proposed, which can reduce drastically the number of features with an acceptable loss of prediction accuracy, and a Genetic Algorithm with a customized cost function is provided to select a small subset of the remainder of features. Expand
Liver Cancer Classification Model Using Hybrid Feature Selection Based on Class-Dependent Technique for the Central Region of Thailand
TLDR
A hybrid feature selection approach by combining information gain and sequential forward selection based on the class-dependent technique (IGSFS-CD) for the liver cancer classification model is proposed to find the best feature subset and to evaluate the classification performance of the predictive model. Expand
Filter based Feature Selection and Ensemble of Classifier for High Dimensional data: Comparative Study
This paper comprehensibly focused on feature selection and classification problems to work with dimensionality. Based on the fact the dimensionality increases the stability of feature selection andExpand
Feature Selection using Genetic Programming
TLDR
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. Expand
A class-specific metaheuristic technique for explainable relevant feature selection
TLDR
This paper seeks to address the problem of identifying explainable features using a class-specific feature selection method based on genetic algorithms and the one-vs-all strategy, and recommends an approach for combining disparate datasets for this purpose. Expand
Feature Selection Techniques for Disease Diagnosis System: A Survey
TLDR
A literature study analyzes some of the existing popular feature selection algorithms and also addresses the strong points and difficulties of those algorithms. Expand
A novel sensitivity-based method for feature selection
TLDR
The implementation of complex-step perturbation in the framework of deep neural networks as a feature selection method is provided in this paper, and its efficacy in determining important features for real-world datasets is demonstrated. Expand
Balancing the user-driven feature selection and their incidence in the clustering structure formation
TLDR
Experimental results and comparisons highlight how the algorithm is robust in the presence of not very significant features, and the classification performance shows the effectiveness of the proposed feature selection method compared with the well-known feature selection algorithms. Expand
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References

SHOWING 1-10 OF 92 REFERENCES
Unsupervised feature selection for multi-cluster data
TLDR
Inspired from the recent developments on manifold learning and L1-regularized models for subset selection, a new approach is proposed, called Multi-Cluster Feature Selection (MCFS), for unsupervised feature selection, which select those features such that the multi-cluster structure of the data can be best preserved. Expand
An unsupervised feature selection algorithm based on ant colony optimization
TLDR
This paper presents an unsupervised feature selection method based on ant colony optimization, called UFSACO, which seeks to find the optimal feature subset through several iterations without using any learning algorithms. Expand
Correlation-based Feature Selection for Machine Learning
TLDR
This thesis addresses the problem of feature selection for machine learning through a correlation based approach with CFS (Correlation based Feature Selection), an algorithm that couples this evaluation formula with an appropriate correlation measure and a heuristic search strategy. Expand
Feature selection for high-dimensional class-imbalanced data sets using Support Vector Machines
TLDR
A backward elimination approach based on successive holdout steps, whose contribution measure is based on a balanced loss function obtained on an independent subset, to address high dimensionality as well as class-imbalance issues. Expand
A Hybrid Feature Selection Method to Improve Performance of a Group of Classification Algorithms
TLDR
A hybrid feature selection method is proposed which takes advantages of wrapper subset evaluation with a lower cost and improves the performance of a group of classifiers and shows a rise in the average performance of five classifiers simultaneously and the classification error for these classifiers decreases considerably. Expand
Feature Selection for Classification
TLDR
This survey identifies the future research areas in feature selection, introduces newcomers to this field, and paves the way for practitioners who search for suitable methods for solving domain-specific real-world applications. Expand
A Fast Clustering-Based Feature Subset Selection Algorithm for High-Dimensional Data
TLDR
The results demonstrate that the FAST not only produces smaller subsets of features but also improves the performances of the four types of classifiers. Expand
Simultaneous feature selection and feature weighting using Hybrid Tabu Search/K-nearest neighbor classifier
TLDR
A novel hybrid approach is proposed for simultaneous feature selection and feature weighting of K-NN rule based on Tabu Search (TS) heuristic, and it is revealed that the proposed hybrid TS heuristic is superior to both simple TS and sequential search algorithms. Expand
Text Clustering with Feature Selection by Using Statistical Data
TLDR
This paper proposes a new supervised feature selection method, named CHIR, which is based on the chi2 statistic and new statistical data that can measure the positive term-category dependency and shows that TCFS with CHIR has better clustering accuracy in terms of the F-measure and the purity. Expand
An evaluation of classifier-specific filter measure performance for feature selection
TLDR
A number of filter-based feature subset evaluation measures are examined with the goal of assessing their performance with respect to specific classifiers, and the results indicate that the best filter measure is classifier-specific. Expand
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