Integrating Instance Selection, Instance Weighting, and Feature Weighting for Nearest Neighbor Classifiers by Coevolutionary Algorithms
نویسندگان
چکیده
منابع مشابه
A Co-evolutionary Framework for Nearest Neighbor Enhancement: Combining Instance and Feature Weighting with Instance Selection
The nearest neighbor rule is one of the most representative methods in data mining. In recent years, a great amount of proposals have arisen for improving its performance. Among them, instance selection is highlighted due to its capabilities for improving the accuracy of the classifier and its efficiency simultaneously, by editing noise and reducing considerably the size of the training set. It...
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In the field of statistical discrimination nearest neighbor methods are a well known, quite simple but successful nonparametric classification tool. In higher dimensions, however, predictive power normally deteriorates. In general, if some covariates are assumed to be noise variables, variable selection is a promising approach. The paper’s main focus is on the development and evaluation of a ne...
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Nearest-neighbor algorithms are known to depend heavily on their distance metric. In this paper, we investigate the use of a weighted Euclidean metric in which the weight for each feature comes from a small set of options. We describe Diet, an algorithm that directs search through a space of discrete weights using cross-validation error as its evaluation function. Although a large set of possib...
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We consider feature selection and weighting for nearest neighbor classifiers. Atechnical challenge in this scenario is how to cope with discrete update of nearestneighbors when the feature space metric is changed during the learning process.This issue, called the target neighbor change, was not properly addressed in theexisting feature weighting and metric learning literature. I...
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ژورنال
عنوان ژورنال: IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)
سال: 2012
ISSN: 1083-4419,1941-0492
DOI: 10.1109/tsmcb.2012.2191953