Improving nearest neighbor classification with simulated gravitational collapse

Authors:
Chen Wang;Yan Qiu Chen
Affiliations:
Department of Computer Science and Engineering, School of Information Science and Engineering, Fudan University, Shanghai, P. R. China;Department of Computer Science and Engineering, School of Information Science and Engineering, Fudan University, Shanghai, P. R. China
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
Year:
2005

Citing 5
Cited 1

Discovering Useful Concept Prototypes for Classification Based on Filtering and Abstraction

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Bootstrap Technique for Nearest Neighbor Classifier Design

IEEE Transactions on Pattern Analysis and Machine Intelligence
Finding Prototypes For Nearest Neighbor Classifiers

IEEE Transactions on Computers
Considerations about sample-size sensitivity of a family of editednearest-neighbor rules

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Face recognition using the nearest feature line method

IEEE Transactions on Neural Networks

Transfer learning for cross-company software defect prediction

Information and Software Technology

Quantified Score

Hi-index	0.00

Visualization

Abstract

The performance of the Nearest Neighbor classifier drops significantly with the increase of the overlapping of the distribution of different classes. To overcome this drawback, we propose to simulate the physical process of gravitational collapse to trim the boundaries of the distribution of each class to reduce overlapping. The proposed simulated gravitational collapse(SGC) algorithm is tested on 7 real-world data sets. Experimental results show that the nearest prototype classifier based on SGC outperforms conventional NN and k-NN classifiers.