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A merge-based condensing strategy for multiple prototype classifiers.

R A Mollineda¹, F J Ferri, E Vidal

¹Inst. Tecnologie d'Informatica, Univ. Politecnica de Valencia, Spain.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|February 5, 2008

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This study introduces a novel class-conditional hierarchical clustering framework for creating efficient prototype classifiers. The method effectively reduces data representation while preserving crucial discriminating information.

Area of Science:

Machine Learning
Pattern Recognition
Data Mining

Background:

Existing condensing schemes for prototype classifiers can be generalized and improved.
Multiple prototype classifiers offer advantages in data representation and classification accuracy.

Purpose of the Study:

To propose a class-conditional hierarchical clustering framework for obtaining multiple prototype classifiers.
To improve upon existing condensing schemes by leveraging geometric properties and clusters.
To efficiently reduce data representation while maintaining discriminating power.

Main Methods:

Utilizing a class-conditional hierarchical clustering framework.
Employing geometric properties and cluster analysis for data reduction.
Generating reduced sets of prototypes that accurately represent the data.

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Main Results:

The proposed method efficiently obtains reduced prototype sets.
The generated prototypes accurately represent the data and retain significant discriminating power.
Empirical assessments demonstrate the benefits of the proposed approach compared to similar algorithms.

Conclusions:

The class-conditional hierarchical clustering framework offers an effective way to develop multiple prototype classifiers.
The method provides a significant improvement in data representation efficiency without compromising classification performance.
This approach generalizes and enhances prior condensing techniques for classifier development.