Combining Weighted SVMs and Spectrum-Based kNN for Multi-classification

Authors:
Ling Ping;Lu Nan;Wang Jian-Yu;Zhou Chun-Guang
Affiliations:
College of Computer Science, Jilin University, Key Laboratory of Symbol Computation, and Knowledge Engineering of the Ministry of Education, Changchun 130012, China and School of Computer Science, ...;College of Computer Science, Jilin University, Key Laboratory of Symbol Computation, and Knowledge Engineering of the Ministry of Education, Changchun 130012, China;College of Computer Science, Jilin University, Key Laboratory of Symbol Computation, and Knowledge Engineering of the Ministry of Education, Changchun 130012, China;College of Computer Science, Jilin University, Key Laboratory of Symbol Computation, and Knowledge Engineering of the Ministry of Education, Changchun 130012, China
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Year:
2007

Citing 7
Cited 0

C4.5: programs for machine learning

C4.5: programs for machine learning
Local learning algorithms

Neural Computation
Discriminant Adaptive Nearest Neighbor Classification

IEEE Transactions on Pattern Analysis and Machine Intelligence
Classification by pairwise coupling

NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
An introduction to support Vector Machines: and other kernel-based learning methods

An introduction to support Vector Machines: and other kernel-based learning methods
On the Learnability and Design of Output Codes for Multiclass Problems

Machine Learning
Support vector clustering

The Journal of Machine Learning Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a Multi-Classification Schema (MCS) which combines Weighted SVMs (WSVM) and Spectrum-based kNN (SkNN). Basic SVM is equipped with belief coefficients to reveal its capacity in identifying classes. And basic SVM is built in individual feature space to bring adaptation to diverse training data context. Coupled with a weighted voting strategy and a local informative metric, SkNN is used to address the case rejected by all basic classifiers. The local metric is derived from most discriminant directions carried by data spectrum information. Two strategies of MCS benefit computational cost: training dataset reduction, and pre-specification of SkNN working set. Experiments on real datasets show MCS improves classification accuracy with moderate cost compared with the state of the art.