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Feature selection using a piecewise linear network.

Jiang Li¹, Michael T Manry, Pramod L Narasimha

¹Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX 76019, USA. li@wcn.uta.edu

IEEE Transactions on Neural Networks

|September 28, 2006

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This study introduces an efficient feature selection algorithm for regression problems using a piecewise linear orthonormal least squares (OLS) method. The approach quickly identifies optimal feature subsets, enhancing predictive model performance.

Area of Science:

Machine Learning
Statistical Modeling
Data Science

Background:

Feature selection is crucial for building efficient and accurate regression models.
Traditional methods can be computationally intensive and suffer from issues like the nesting effect.

Purpose of the Study:

To develop a computationally efficient feature selection algorithm for general regression.
To address limitations of existing methods, such as the nesting effect.

Main Methods:

Utilized a piecewise linear orthonormal least squares (OLS) procedure.
Developed a piecewise linear network (PLN) model selection component.
Employed a floating search algorithm to avoid the nesting effect and identify feature subsets.

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Main Results:

The proposed algorithm demonstrates high computational efficiency, requiring only a single data pass.
Effectiveness was validated through several illustrative examples.
The floating search successfully prevented the nesting effect in feature selection.

Conclusions:

The presented algorithm offers an efficient and effective solution for feature selection in regression tasks.
The combination of PLN modeling, OLS, and floating search provides a robust methodology.
This approach has the potential to improve the performance and reduce the complexity of regression models.