An affinity-based new local distance function and similarity measure for kNN algorithm

Authors:
Gautam Bhattacharya;Koushik Ghosh;Ananda S. Chowdhury
Affiliations:
Department of Physics, University Institute of Technology, University of Burdwan, Golapbag (North), Burdwan 713104, India;Department of Mathematics, University Institute of Technology, University of Burdwan, Golapbag (North), Burdwan 713104, India;Department of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata 700032, India
Venue:
Pattern Recognition Letters
Year:
2012

Citing 14
Cited 1

Discriminant Adaptive Nearest Neighbor Classification

IEEE Transactions on Pattern Analysis and Machine Intelligence
Locally Weighted Learning

Artificial Intelligence Review - Special issue on lazy learning
A re-examination of text categorization methods

Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Unsupervised Feature Selection Using Feature Similarity

IEEE Transactions on Pattern Analysis and Machine Intelligence
Density-Based Multiscale Data Condensation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Locally Adaptive Metric Nearest-Neighbor Classification

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

Pattern Classification (2nd Edition)
Formulating distance functions via the kernel trick

Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Improving nearest neighbor rule with a simple adaptive distance measure

Pattern Recognition Letters
Information-theoretic metric learning

Proceedings of the 24th international conference on Machine learning
Creating diverse nearest-neighbour ensembles using simultaneous metaheuristic feature selection

Pattern Recognition Letters
Similarity Search: The Metric Space Approach

Similarity Search: The Metric Space Approach
k-nearest-neighbor Bayes-risk estimation

IEEE Transactions on Information Theory
A learning scheme for a fuzzy k-NN rule

Pattern Recognition Letters

Fault diagnosis of rolling element bearing with intrinsic mode function of acoustic emission data using APF-KNN

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.10

Visualization

Abstract

In this paper, we propose a modified version of the k-nearest neighbor (kNN) algorithm. We first introduce a new affinity function for distance measure between a test point and a training point which is an approach based on local learning. A new similarity function using this affinity function is proposed next for the classification of the test patterns. The widely used convention of k, i.e., k=[@/N] is employed, where N is the number of data used for training purpose. The proposed modified kNN algorithm is applied on fifteen numerical datasets from the UCI machine learning data repository. Both 5-fold and 10-fold cross-validations are used. The average classification accuracy, obtained from our method is found to exceed some well-known clustering algorithms.