A new fuzzy rule-based initialization method for k-nearest neighbor classifier

TW Chua, WW Tan - 2009 IEEE International Conference on …, 2009 - ieeexplore.ieee.org
The performances of conventional crisp and fuzzy K-nearest neighbor (K-NN) algorithms
trained using finite samples tends to be poor. With ldquoholesrdquo in the training data, it is …
被引用次数：17 相关文章

A new fuzzy rule-based initialization method for K-nearest neighbor classifier

TW Chua, WW Tan - Proceedings of the 18th international conference …, 2009 - dl.acm.org
The performances of conventional crisp and fuzzy K-Nearest neighbor (K-NN) algorithms
trained using finite samples tends to be poor [1],[2]. With" holes" in the training data, it is …

A new fuzzy rule-based initialization method for K-Nearest neighbor classifier

TW Chua, WW Tan - 2009 IEEE International Conference on Fuzzy Systems - infona.pl
The performances of conventional crisp and fuzzy K-nearest neighbor (K-NN) algorithms
trained using finite samples tends to be poor. With ldquoholesrdquo in the training data, it is …

A new fuzzy rule-based initialization method for K-Nearest neighbor classifier

T Chua, W Tan - 2009 - scholarbank.nus.edu.sg
The performances of conventional crisp and fuzzy K-Nearest neighbor (K-NN) algorithms
trained using finite samples tends to be poor [1],[2]. With" holes" in the training data, it is …
[PDF] researchgate.net

[PDF][PDF] A New Fuzzy Rule-Based Initialization Method for K-Nearest Neighbor Classifier

TW Chua, WW Tan - researchgate.net
K-Nearest neighbor (K-NN) algorithms trained using finite samples tends to be poor [1],[2].
With “holes” in the training data, it is unlikely that the decision area formed can actually …