Letters: Adaptive local hyperplane classification

Authors:
Tao Yang;Vojislav Kecman
Affiliations:
School of Engineering, The University of Auckland, Auckland, New Zealand;School of Engineering, The University of Auckland, Auckland, New Zealand
Venue:
Neurocomputing
Year:
2008

Citing 4
Cited 12

Discriminant Adaptive Nearest Neighbor Classification

IEEE Transactions on Pattern Analysis and Machine Intelligence
An ensemble of K-local hyperplanes for predicting protein--protein interactions

Bioinformatics
Rapid and brief communication: Center-based nearest neighbor classifier

Pattern Recognition
Face recognition using the nearest feature line method

IEEE Transactions on Neural Networks

Protein fold recognition with adaptive local hyperplane algorithm

CIBCB'09 Proceedings of the 6th Annual IEEE conference on Computational Intelligence in Bioinformatics and Computational Biology
Adaptive local hyperplane for regression tasks

IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Research frontier: building virtual community in computational intelligence and machine learning

IEEE Computational Intelligence Magazine
Fast and Scalable Local Kernel Machines

The Journal of Machine Learning Research
Adaptive local hyperplanes for MTV affective analysis

ICIMCS '10 Proceedings of the Second International Conference on Internet Multimedia Computing and Service
Margin-based ensemble classifier for protein fold recognition

Expert Systems with Applications: An International Journal
Improvements over adaptive local hyperplane to achieve better classification

ICDM'11 Proceedings of the 11th international conference on Advances in data mining: applications and theoretical aspects
A novel prototype reduction method for the K-nearest neighbor algorithm with K≥1

PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Feature weighting by RELIEF based on local hyperplane approximation

PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Perceptual relativity-based local hyperplane classification

Neurocomputing
Random subspace evidence classifier

Neurocomputing
Cognitive gravitation model for classification on small noisy data

Neurocomputing

Quantified Score

Hi-index	0.01

Visualization

Abstract

In this paper, a novel classifier, called adaptive local hyperplane, is proposed for pattern classification. The experimental results on 11 real data sets demonstrate that the proposed classifier outperforms, on average, all the other seven benchmarking classifiers. In particular, it is the best classifier in 10 out of 11 data sets, and it is the close second best for just one data set.