Boosting the Performance of Nearest Neighbour Methods with Feature Selection

Authors:
Shlomo Geva
Affiliations:
-
Venue:
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
Year:
2001

Citing 5
Cited 0

Tolerating noisy, irrelevant and novel attributes in instance-based learning algorithms

International Journal of Man-Machine Studies - Special issue: symbolic problem solving in noisy and novel task environments
C4.5: programs for machine learning

C4.5: programs for machine learning
A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features

Machine Learning
Feature Selection for Knowledge Discovery and Data Mining

Feature Selection for Knowledge Discovery and Data Mining
Improved use of continuous attributes in C4.5

Journal of Artificial Intelligence Research

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Abstract

This paper describes a Nearest Neighbour procedure for variable selection in function approximation, pattern classification, and time series prediction. Given a training set of input/output vector pairs the procedure identifies a subset of input vector components that effectively capture the input-output relationship implicit in the training set. The utility of this procedure is demonstrated with numerous data sets from the UCI repository of machine learning databases and the Mackey-Glass time series prediction. A comprehensive set of benchmark problems is used to demonstrate comparable performance to that of much more complex boosted C4.5 decision trees.