Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Machine Learning
Discriminant Adaptive Nearest Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local dimensionality reduction
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Prediction games and arcing algorithms
Neural Computation
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Feature Extraction, Construction and Selection: A Data Mining Perspective
Feature Extraction, Construction and Selection: A Data Mining Perspective
Machine Learning
A Practical Approach to Feature Selection
ML '92 Proceedings of the Ninth International Workshop on Machine Learning
Response shrinkage estimators in binary regression
Computational Statistics & Data Analysis
Benchmarking local classification methods
Computational Statistics
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The main problem with localized discriminant techniques is the curse of dimensionality, which seems to restrict their use to the case of few variables. However, if localization is combined with a reduction of dimension the initial number of variables is less restricted. In particular it is shown that localization yields powerful classifiers even in higher dimensions if localization is combined with locally adaptive selection of predictors. A robust localized logistic regression (LLR) method is developed for which all tuning parameters are chosen data-adaptively. In an extended simulation study we evaluate the potential of the proposed procedure for various types of data and compare it to other classification procedures. In addition we demonstrate that automatic choice of localization, predictor selection and penalty parameters based on cross validation is working well. Finally the method is applied to real data sets and its real world performance is compared to alternative procedures.