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SMO-based pruning methods for sparse least squares support vector machines.

Xiangyan Zeng¹, Xue-Wen Chen

¹Department of Electrical and Computer Engineering, California State University, Northridge, CA 91003, USA.

IEEE Transactions on Neural Networks

|December 14, 2005

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This study introduces a novel pruning algorithm for sparse least squares support vector machines (LS-SVMs). The new method enhances computational efficiency and classification accuracy by optimizing data point selection during pruning.

Area of Science:

Machine Learning
Computational Statistics

Background:

Least Squares Support Vector Machines (LS-SVMs) often yield non-sparse solutions.
Achieving sparseness typically involves iterative retraining, which is computationally intensive.

Purpose of the Study:

To develop a computationally efficient pruning algorithm for sparse LS-SVMs.
To improve classification accuracy while reducing computational cost.

Main Methods:

Introduced the sequential minimal optimization (SMO) method into the LS-SVM pruning process.
Proposed a new criterion for data point omission based on minimal changes to the dual objective function, rather than training errors.

Main Results:

The proposed pruning algorithm demonstrates significant computational efficiency compared to iterative retraining methods.

Related Experiment Videos

Numerical experiments confirm the effectiveness of the new method in enhancing classification accuracy.

The criterion based on dual objective function changes is computationally efficient.

Conclusions:

The novel pruning algorithm offers an effective and efficient approach to generating sparse LS-SVMs.
The method balances computational cost and classification performance, making it suitable for large datasets.