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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Design of feature selection algorithm for high-dimensional network data based on supervised discriminant projection.

Zongfu Zhang¹, Qingjia Luo², Zuobin Ying²

¹College of Information Engineering, Jiangmen Polytechnic, Jiangmen, China.

Peerj. Computer Science

|July 6, 2023

Summary

This summary is machine-generated.

This study introduces a novel feature selection algorithm for high-dimensional network data, improving accuracy and efficiency. The supervised discriminant projection (SDP) method effectively handles complex data, achieving high performance metrics.

Keywords:

Feature selection Network high-dimensional data Sparse constraint Sparse subspace clustering Supervised discriminant projection

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Area of Science:

Data Science
Network Analysis
Machine Learning

Background:

High-dimensional network data presents challenges for effective feature selection due to its complexity.
Existing methods often struggle with the scale and intricacy of network data, leading to suboptimal feature identification.

Purpose of the Study:

To develop an effective feature selection algorithm for high-dimensional network data.
To address the limitations of current methods in handling complex network structures and large datasets.

Main Methods:

Designed feature selection algorithms based on supervised discriminant projection (SDP).
Transformed the sparse representation problem into an Lp norm optimization problem.
Utilized sparse subspace clustering and dimensionless processing combined with SDP for feature reduction and selection.

Main Results:

The algorithm effectively clusters seven different data types, converging around 24 iterations.
Achieved high levels of F1 score, recall, and precision.
Demonstrated an average feature selection accuracy of 96.9% with an average time of 65.1 milliseconds.

Conclusions:

The proposed algorithm offers a robust solution for feature selection in high-dimensional network data.
The method proves effective in enhancing data clustering and achieving accurate feature identification.
The algorithm shows significant improvements in both accuracy and efficiency compared to existing approaches.