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Related Experiment Videos

Data dimensionality reduction with application to simplifying RBF network structure and improving classification

Xiuju Fu¹, Lipo Wang

¹Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore, Singapore.

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

|February 2, 2008

Summary

This summary is machine-generated.

Related Concept Videos

Block Diagram Reduction

Block Diagram Reduction

The process of deriving the transfer function of a control system often involves reducing its block diagram to a single block. This simplification can be achieved through a series of strategic operations, including relocating branch points and comparators. These operations preserve the overall function of the system while allowing for easier manipulation and combination of blocks.
The first step in this process is the identification and relocation of a branch point. A branch point, where a...

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This study introduces a new separability-correlation measure (SCM) for ranking attribute importance in high-dimensional data. The method reduces classifier complexity and improves accuracy by selecting relevant features for radial basis function (RBF) networks.

Area of Science:

Machine Learning
Data Science
Computer Science

Background:

High-dimensional data presents computational challenges for classifiers like radial basis function (RBF) neural networks.
Irrelevant attributes can increase computational load and decrease classification accuracy.
Existing attribute selection methods may not be optimal for reducing classifier complexity.

Purpose of the Study:

To propose a novel separability-correlation measure (SCM) for ranking attribute importance.
To reduce the computational complexity and improve the classification performance of RBF networks.
To develop an efficient method for constructing RBF classifiers with reduced structural complexity.

Main Methods:

A novel separability-correlation measure (SCM) is proposed to rank attribute importance.

Related Experiment Videos

Attribute subsets are selected based on ranking, and those increasing validation error are removed.

A modified method for efficient RBF classifier construction allows for larger cluster overlaps.

Main Results:

The SCM method yields smaller attribute subsets with higher classification accuracies compared to SUD and Relief-F.
The proposed approach significantly reduces the structural complexity of RBF networks.
Classification performance is improved through efficient feature selection and RBF network construction.

Conclusions:

The SCM method is effective for attribute importance ranking in high-dimensional data.
Feature selection using SCM enhances RBF network efficiency and accuracy.
The modified RBF construction method further improves performance and reduces network complexity.