Intelligent Database Systems Lab

國立雲林科技大學National Yunlin University of Science and Technology

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A Hybrid Supervised ANN for Classification and Data Visualization

Chee Siong Teh and Md. Sarwar Zahan Tapan

Presented by Jun-Yi Wu2010/09/28

2008 IEEE

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Outlines

Motivation Objectives Methodology Experiments Conclusions Comments

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Motivation

ANN usually do not support supervised classification and data visualization simultaneously.

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Objectives

To propose a novel hybrid supervised ANN of Learning Vector Quantization(LVQ) with Adaptive Coordinate (AC) by hybridizing LVQ and modified AC approach.

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I. M.Methodology

A.Learning Vector Quantization(LVQ)

B.Adaptive Coordinates(AC)

C.Modified adaptation criteria of AC

D.LVQ with AC Alogorithm

E.Cost function of LVQ with AC

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I. M.Methodology

Learning Vector Quantization(LVQ)

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I. M.Methodology

Adaptive Coordinates(AC)

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I. M.Methodology

Modified adaptation criteria of AC

The proposed modification of AC is to ensure the integration of AC and LVQ enables the proposed method with data visualization.

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I. M.Methodology

LVQ with AC Alogorithm

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I. M.Methodology

Cost function of LVQ with AC

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I. M.Experiments

A.Visualization evaluation using a 3D synthetic data set

B.Visualization evaluation using WBC data set

C.Visualization evaluation using Wine data set

D.Quantitative evaluation of LVQ with AC’s visualization

E.Classification evaluation

F.Network utilization and dead neuron

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I. M.Experiments Visualization evaluation using a 3D synthetic

data set

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I. M.Experiments

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I. M.Experiments Visualization evaluation using WBC data set

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I. M.Experiments Visualization evaluation using Wine data set

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I. M.Experiments Quantitative evaluation of LVQ with AC’s

visualization

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I. M.Experiments Classification evaluation

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I. M.Experiments Network utilization and dead neuron

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I. M.Conclusion

Empirical studies show that LVQwithAC be able to provide promising classification simultaneously with effective visualizations.

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Comments

Advantages─ map.

Applications─ Classification