K-D Decision Tree: An Accelerated and Memory Efficient Nearest Neighbor Classifier

Authors:
Tomoyuki Shibata;Takekazu Kato;Toshikazu Wada
Affiliations:
-;-;-
Venue:
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Year:
2003

Citing 6
Cited 0

Instance-Based Learning Algorithms

Machine Learning
The nature of statistical learning theory

The nature of statistical learning theory
A decision-theoretic generalization of on-line learning and an application to boosting

Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
An optimal algorithm for approximate nearest neighbor searching fixed dimensions

Journal of the ACM (JACM)
Multidimensional binary search trees used for associative searching

Communications of the ACM
Noise-tolerant instance-based learning algorithms

IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1

Quantified Score

Hi-index	0.00

Visualization

Abstract

Most nearest neighbor (NN) classifiers employ NN searchalgorithms for the acceleration. However, NNclassification does not always require the NN search.Based on this idea, we propose a novel algorithm namedk-d decision tree (KDDT). Since KDDT uses Voronoicondensed prototypes, it is less memory consuming thannaive NN classifiers. We have confirmed that KDDT ismuch faster than NN search based classifiers through thecomparative experiment (from 9 to 369 times faster).