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Efficient Nearest Neighbor Classification with Vector Quantization Method

This study introduces a vector quantization method for designing a nearest neighbor classifier, aiming to improve accuracy with minimal prototypes. Experimental results on various data sets validate the effectiveness of the proposed approach.

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Efficient Nearest Neighbor Classification with Vector Quantization Method

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  1. A vector quantization method for nearest neighbor classifier design Source: Pattern Recognition Letters, Vol. 25, 2004, pp. 725-731 Author: Chen-Wen Yen, Chieh-Neng Young and Mark L. Nagurka Speaker: Guey-Tzu Chang Date: May 17, 2004 國立中正大學資訊工程所

  2. Class 1 Class 2 Classifier Condensed Nearest Neighbor (CNN), Hart, 1968 : Class n VQ Nearest Neighbor (VQ-NN), Xie, 1993 : Adaptive VQ Nearest Neighbor (AVQ-NN), Yen et al., 2004 Nearest neighbor data 國立中正大學資訊工程所

  3. Nearest neighbor (Cont.) • Factors: • Accuracy • Number of prototypes 國立中正大學資訊工程所

  4. CNN 國立中正大學資訊工程所

  5. VQ-NN Class 1 Class 1 Class 2 Class 2 : : Class n Class n Code book (prototype set) 國立中正大學資訊工程所

  6. VQ-NN (Cont.) • Drawback: • It does not consider the interaction among different classes of samples • Difficult to design an NN classifier that has an optimal number of prototypes 國立中正大學資訊工程所

  7. Validation set Training set Class 1 Class 1 Class 2 : Cluster NN Cluster-NN Class 2 : 2 Class n 1 prototype set (initial) Class n prototype set 0 Error < th N retraining Y terminate AVQ-NN 國立中正大學資訊工程所

  8. Experimental results Smaller data sets: Wisconsin breast cancer Australian credit card 國立中正大學資訊工程所

  9. Experiment results (Cont.) Large data sets: Phoneme Kr-vs-Kp 國立中正大學資訊工程所

  10. Conclusion • The proposed approach can achieve high classification accuracy with a relatively small number of prototypes. • Another possible future direction is to investigate the sensitivity of the proposed method to the training set size. 國立中正大學資訊工程所

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