on utillizing lvq3-type algorithms to enhance prototype reduction schemes sang-woon kim and b. john...

On Utillizing LVQ3-Type Algorithms to Enhance Prototype Reduction

Schemes

Sang-Woon Kim and B. John Oommen*

Myongji University, Carleton University*

Workshop on PRIS’ 2002

Outline of the Study

Introduction Overview of the Prototype Reduction

Schemes The Proposed Reduction Method Experiments & Discussions Conclusions


Introduction (1)

The Nearest Neighbor (NN) Classifier : A widely used classifier, which is simple and

yet one of the most efficient classification rules in practice.

However, its application often suffers from the computational complexity caused by the huge amount of information.


Introduction (2)

Solving strategies to the problem : Reducing the size of the design set without

sacrificing the performance. Accelerating the speed of computation by

eliminating the necessity of calculating many distances.

Increasing the accuracy of the classifiers designed with limited samples.


Motivation of the Study

In NN classifications, prototypes near the boundary play more important roles.

The prototypes need to be moved or adjusted towards the classification boundary.

The proposed approach is based on this philosophy, namely that of creating and adjusting.


Prototype Reduction Schemes- Conventional Approaches -

The Condensed Nearest Neighbor (CNN) : The RNN, SNN, ENN, mCNN rules

The Prototypes for Nearest Neighbor (PNN) classifiers

The Vector Quantization (VQ) & Bootstrap (BT) techniques

The Support Vector Machines (SVM)


A Graphical Example (PNN)


LVQ3 Algorithm

An improved LVQ algorithm :

Learning Parameters : Initial vectors Learning rates : Iteration numbers

Training Set = Placement + Optimizing:


Support Vector Machines (SVM)

The SVM has a capability of extracting vectors which support the boundary between two classes, and they can satisfactorily represent the global distribution structure.


Extension by Kernels


The Proposed Method

First, the CNN, PNN, VQ, SVM are employed to select initial prototype vectors.

Next, an LVQ3-type learning is performed to adjust the prototypes: Perform the LVQ3 with Tip to select w Perform the LVQ3 with Tip to select e Repeat the above steps to obtain the best w* and e*

Finally, determine the best prototypes by invoking the learning n times with Tip and Tio.


Experiments

The proposed method is tested with artificial and real benchmark design data sets, and compared with the conventional methods.

The one-against-all NN classifier is designed.

Benchmark data sets :


Experimental Results (3)


Experimental Results (4)


Data Compression Rates


Classification Error Rates (%)- Before Adjusting -


Classification Error Rates (%)- After Adjusting with LVQ3 -


Conclusions

The method provides a principled way of choosing prototype vectors for designing NN classifiers.

The performance of a classifier trained with the method is better than that of the CNN, PNN, VQ, and SVM classifier.

The future work is to expand this study into large data set problems such as data mining and text categorization.

on utillizing lvq3-type algorithms to enhance prototype reduction schemes sang-woon kim and b. john...

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