Comparative Study of Feature Subset Selection Methods for Dimensionality Reduction on Scientific Data

Padmaja, D. Lakshmi; Vishnuvardhan, B.

doi:10.1109/iacc.2016.16

Cited by 34 publications

(21 citation statements)

References 8 publications

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“…Feature selection aims to select a feature subset from the original set of features based on a/the feature's relevance and redundancy. Originally evaluation methods in feature selection are divided into four kinds: filter, wrapper, embedded [10,14,18], and hybrid [20,29]. Recently, another type of evaluation method is developed, i.e., ensemble feature selection [30,31]. "…”

Section: Feature Selection Methodsmentioning

confidence: 99%

See 1 more Smart Citation

A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction

Zebari¹,

Abdulazeez²,

Zeebaree³

et al. 2020

JASTT

548

210

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Due to sharp increases in data dimensions, working on every data mining or machine learning (ML) task requires more efficient techniques to get the desired results. Therefore, in recent years, researchers have proposed and developed many methods and techniques to reduce the high dimensions of data and to attain the required accuracy. To ameliorate the accuracy of learning features as well as to decrease the training time dimensionality reduction is used as a pre-processing step, which can eliminate irrelevant data, noise, and redundant features. Dimensionality reduction (DR) has been performed based on two main methods, which are feature selection (FS) and feature extraction (FE). FS is considered an important method because data is generated continuously at an ever-increasing rate; some serious dimensionality problems can be reduced with this method, such as decreasing redundancy effectively, eliminating irrelevant data, and ameliorating result comprehensibility. Moreover, FE transacts with the problem of finding the most distinctive, informative, and decreased set of features to ameliorate the efficiency of both the processing and storage of data. This paper offers a comprehensive approach to FS and FE in the scope of DR. Moreover, the details of each paper, such as used algorithms/approaches, datasets, classifiers, and achieved results are comprehensively analyzed and summarized. Besides, a systematic discussion of all of the reviewed methods to highlight authors' trends, determining the method(s) has been done, which significantly reduced computational time, and selecting the most accurate classifiers. As a result, the different types of both methods have been discussed and analyzed the findings.

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Section: Feature Selection Methodsmentioning

confidence: 99%

“…Feature selection is utilized to reduce the dimensionality impact on the dataset through finding the subset of feature which efficiently define the data [18,19]. It selects the important and relevant features to the mining task from the input data and removes redundant and irrelevant features [20,21].…”

Section: A Feature Selectionmentioning

confidence: 99%

A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction

Zebari¹,

Abdulazeez²,

Zeebaree³

et al. 2020

JASTT

548

210

View full text Add to dashboard Cite

show abstract

“…As a result of a sequential feature selection (SFS) [14], the most useful solution proved to be combining global feature systems: First Order Statistics (FOS), Contour First Order Statistics (CFOS) and Local Range Filter (LRF). The types of the FOS features selected by SFS are mean, standard deviation, kurtosis and skewness.…”

Section: Resultsmentioning

confidence: 99%

Amber Gemstones Sorting By Colour

Sinkevicius¹,

Lipnickas²,

Rimkus³

2017

ElAEE

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The objective of this study is to create computer vision algorithms for autonomous multiclass identification of amber nuggets by their colour. By applying the proposed methods an automated production sorting system has been developed. This system can be used, for example in combination with conveyor systems, and in any other case that requires distinguishing objects of many classes in a high-rate flow of objects. In order to achieve this, the proposed system operates with colour features selection, algorithm for classifier training, grouping, and voting with reject option have been developed. The developed system has been used in an automated amber sorting line to increase the quantities of sorted amber nuggets. The applied algorithms gave 88.21% as the highest accuracy for the amber nugget expert database consisting of 30 classes.

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“…In my previous paper [9] gives a comparative study of traditional feature subset selection techniques like Sequential Forward Selection (SFS), and Sequential Floating Forward Selection (SFFS) with RSFS. The SFS follows the procedure to find next best feature compared to the existing feature set as in forward selection(top down approach) [10], but it suffers with nesting problem, means; once a feature is retained, it cannot be discarded; Sequential Backward Selection(SBS), removing the worst feature as in backward selection(bottom up approach) suffers with more computation problem than SFS and nesting problem; SFFS is to avoid the problem of nesting of features by doing both(SFS & SBS) in a recursive manner [11].…”

Section: Random Subset Feature Selection Comparison With Traditional mentioning

confidence: 99%

Influence of Data Geometry in Random Subset Feature Selection

D¹,

Vishnuvardhan²

2017

IJDKP

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Comparative Study of Feature Subset Selection Methods for Dimensionality Reduction on Scientific Data

Cited by 34 publications

References 8 publications

A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction

A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction

Amber Gemstones Sorting By Colour

Influence of Data Geometry in Random Subset Feature Selection

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