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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Discriminative least squares regression for multiclass classification and feature selection.

Shiming Xiang, Feiping Nie, Gaofeng Meng

IEEE Transactions on Neural Networks and Learning Systems

|May 9, 2014

Summary

This study introduces discriminative least squares regression (LSR) for multiclass classification and feature selection. The novel ε-dragging technique enhances class separation, creating a compact, efficient model for improved data analysis.

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Area of Science:

Machine Learning
Computer Science

Background:

Multiclass classification and feature selection are crucial in machine learning.
Existing methods often involve complex, independent two-class models.

Purpose of the Study:

To develop a unified framework for multiclass classification and feature selection.
To enhance class separability within the least squares regression (LSR) framework.

Main Methods:

Introduced a novel ε-dragging technique to enlarge distances between class regression targets.
Integrated ε-dragging into LSR for a compact multiclass classification model.
Extended the model for feature selection using the L2,1 norm for sparsity.

Main Results:

The discriminative LSR framework offers a compact model without independent two-class classifiers.
The L2,1 norm extension effectively performs sparse feature selection.
Experimental results on benchmark datasets validate the proposed method's effectiveness.

Conclusions:

The discriminative LSR framework provides an efficient and unified approach to multiclass classification and feature selection.
The ε-dragging technique is effective in improving class discrimination.
The method demonstrates strong performance and validity across various datasets.