Effects of Distance Measure Choice on K-Nearest Neighbor Classifier Performance: A Review
نویسندگان
چکیده
منابع مشابه
Distance and Similarity Measures Effect on the Performance of K-Nearest Neighbor Classifier - A Review
The K-nearest neighbor (KNN) classifier is one of the simplest and most common classifiers, yet its performance competes with the most complex classifiers in the literature. The core of this classifier depends mainly on measuring the distance or similarity between the tested example and the training examples. This raises a major question about which distance measures to be used for the KNN clas...
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In this paper, we develop a novel Distance-weighted k -nearest Neighbor rule (DWKNN), using the dual distance-weighted function. The proposed DWKNN is motivated by the sensitivity problem of the selection of the neighborhood size k that exists in k -nearest Neighbor rule (KNN), with the aim of improving classification performance. The experiment results on twelve real data sets demonstrate that...
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One of the difficulties that arises when using the K-nearest neighbor rule is that each of the labeled training samples is given equal importance in deciding the class of the query pattern to be classified, regardless of their typicality. In this paper, the theory of belief functions is introduced into the K-nearest neighbor rule to develop an evidential editing version of this algorithm. An ev...
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Nearest neighbor classification is one of the simplest and popular methods for statistical pattern recognition. It classifies an observation x to the class, which is the most frequent in the neighborhood of x. The size of this neighborhood is usually determined by a predefined parameter k. Normally, one uses cross-validation techniques to estimate the optimum value of this parameter, and that e...
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ژورنال
عنوان ژورنال: Big Data
سال: 2019
ISSN: 2167-6461,2167-647X
DOI: 10.1089/big.2018.0175