Discriminant Adaptive Nearest Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Locally Adaptive Metric Nearest-Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improving nearest neighbor rule with a simple adaptive distance measure
Pattern Recognition Letters
Distance Metric Learning for Large Margin Nearest Neighbor Classification
The Journal of Machine Learning Research
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Bayesian adaptive nearest neighbor
Statistical Analysis and Data Mining
A Field Guide to Genetic Programming
A Field Guide to Genetic Programming
Evolutionary learning of technical trading rules without data-mining bias
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
A gaussian groundplan projection area model for evolving probabilistic classifiers
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Early stopping criteria to counteract overfitting in genetic programming
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Maximum margin decision surfaces for increased generalisation in evolutionary decision tree learning
EuroGP'11 Proceedings of the 14th European conference on Genetic programming
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part II
Adaptive nearest neighbor classifier based on supervised ellipsoid clustering
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
Unsupervised problem decomposition using genetic programming
EuroGP'10 Proceedings of the 13th European conference on Genetic Programming
LDA/SVM driven nearest neighbor classification
IEEE Transactions on Neural Networks
Large margin nearest neighbor classifiers
IEEE Transactions on Neural Networks
Controlling overfitting in symbolic regression based on a bias/variance error decomposition
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
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Nearest Neighbour (NN) classification is a widely-used, effective method for both binary and multi-class problems. It relies on the assumption that class conditional probabilities are locally constant. However, this assumption becomes invalid in high dimensions, and severe bias can be introduced, which degrades the performance of the method. The employment of a locally adaptive distance metric becomes crucial in order to keep class conditional probabilities approximately uniform, whereby better classification performance can be attained. This paper presents a locally adaptive distance metric for NN classification based on a supervised learning algorithm (Genetic Programming) that learns a vector of feature weights for the features composing an instance query. Using a weighted Euclidean distance metric, this has the effect of adaptive neighbourhood shapes to query locations, stretching the neighbourhood along the directions for which the class conditional probabilities don't change much. Initial empirical results on a set of real-world classification datasets showed that the proposed method enhances the generalisation performance of standard NN algorithm, and that it is a competent method for pattern classification as compared to other learning algorithms.