Pattern synthesis using fuzzy partitions of the feature set for nearest neighbor classifier design

Authors:
Pulabaigari Viswanath;S. Chennakesalu;R. Rajkumar;M. Raja Sekhar
Affiliations:
Department of Computer Science and Engineering, Rajeev Gandhi Memorial College of Engineering & Technology, Nandyal, A.P., India;Department of Computer Science and Engineering, Rajeev Gandhi Memorial College of Engineering & Technology, Nandyal, A.P., India;Department of Computer Science and Engineering, Rajeev Gandhi Memorial College of Engineering & Technology, Nandyal, A.P., India;Department of Computer Science and Engineering, Rajeev Gandhi Memorial College of Engineering & Technology, Nandyal, A.P., India
Venue:
MIWAI'11 Proceedings of the 5th international conference on Multi-Disciplinary Trends in Artificial Intelligence
Year:
2011

Citing 8
Cited 0

Data mining: concepts and techniques

Data mining: concepts and techniques
Neural Networks for Pattern Recognition

Neural Networks for Pattern Recognition
A Bootstrap Technique for Nearest Neighbor Classifier Design

IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
A Pattern Synthesis Technique with an Efficient Nearest Neighbor Classifier for Binary Pattern Recognition

ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Partition based pattern synthesis technique with efficient algorithms for nearest neighbor classification

Pattern Recognition Letters
Overlap pattern synthesis with an efficient nearest neighbor classifier

Pattern Recognition
An efficient parzen-window based network intrusion detector using a pattern synthesis technique

PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

Nearest neighbor classifiers require a larger training set in order to achieve a better classification accuracy. For a higher dimensional data, if the training set size is small, it suffers from the curse of dimensionality effect and performance gets degraded. Partition based pattern synthesis is an existing technique of generating a larger set of artificial training patterns based on a chosen partition of the feature set. If the blocks of the partition are statistically independent then the quality of synthetic patterns generated is high. But, such a partition, often does not exist for real world problems. So, approximate ways of generating a partition based on correlation coefficient values between pairs of features were used earlier in some studies. That is, an approximate hard partition, where each feature belongs to exactly one cluster (block) of the partition was used for doing the synthesis. The current paper proposes an improvement over this. Instead of having a hard approximate partition, a soft approximate partition based on fuzzy set theory could be beneficial. The present paper proposes such a fuzzy partitioning method of the feature set called fuzzy partition around medoids (fuzzy-PAM). Experimentally, using some standard data-sets, it is demonstrated that the fuzzy partition based synthetic patters are better as for as the classification accuracy is concerned.