Review on Feature Selection Techniques of DNA Microarray Data

Ammu, P K; Preeja,

doi:10.5120/9983-4814

Cited by 9 publications

(2 citation statements)

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“…𝑘∈𝑁 𝑖 (9) where 𝑁 𝑖 denotes the set of neighbors of 𝑥 𝑖 . The purpose of NCA is to learn a linear transform 𝐴 that maximizes log probability by selecting each data point as neighbors with the same labels as itself after the transformation [46].…”

Section: ) Neighborhood Component Analysismentioning

confidence: 99%

“…One of the important problems with these methods is to select multiple features carrying the same information when strong correlations exist between features unnecessarily. For example, high-dimensional and strongly correlated data such as reflectance, image, text or DNA microarray data problem arises that hindering the learning process, especially in classification [6,[8][9][10][11]. The focus of this study is to determine the variables that will give the highest classification accuracy in classification of high dimensional data.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Approach to Increase the Efficiency of Filter-Based Feature Selection Methods in High-Dimensional Datasets With Strong Correlation Structure

Akogul

2023

IEEE Access

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Nowadays, data dimensions have increased depending on the developments in the information and measurement technologies. Due to the high dimensionality, it is necessary to use pre-analysis data reduction methods for many analyzes such as classification and regression analysis. In the solution of high dimensionality, filter feature selection methods based on statistical criteria are widely used in terms of simplicity and efficiency. One of the important problems with filter feature selection methods is to select multiple features carrying the same information unnecessarily when strong correlations exist between features. In this study, a novel approach is proposed to solve this problem of the filter feature selection methods. In addition, with the proposed new approach, the question of how many appropriate features will be is also solved. The performance of the proposed approach is demonstrated on high-dimensional reflectance data having high correlations between features. The obtained results revealed that the proposed approach improves the classification performance of filter feature selection methods in mixture discriminant analysis in terms of classification accuracy and entropy criteria.

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Section: ) Neighborhood Component Analysismentioning

confidence: 99%