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2020
Please download the sample Excel files from https://github.com/hhohho/Learn-Data-Mining-through-Excel for this chapter’s exercises.
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Please download the sample Excel files from https://github.com/hhohho/Learn-Data-Mining-through-Excel for this chapter’s exercises.
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EVOLVING EDITED k-NEAREST NEIGHBOR CLASSIFIERS
International Journal of Neural Systems, 2008The k-nearest neighbor method is a classifier based on the evaluation of the distances to each pattern in the training set. The edited version of this method consists of the application of this classifier with a subset of the complete training set in which some of the training patterns are excluded, in order to reduce the classification error rate.
Roberto, Gil-Pita, Xin, Yao
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Weighted K-Nearest Neighbor revisited
2016 23rd International Conference on Pattern Recognition (ICPR), 2016In this paper we show that weighted K-Nearest Neighbor, a variation of the classic K-Nearest Neighbor, can be reinterpreted from a classifier combining perspective, specifically as a fixed combiner rule, the sum rule. Subsequently, we experimentally demonstrate that it can be rather beneficial to consider other combining schemes as well. In particular,
BICEGO, Manuele, Loog, M.
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Multiple k nearest neighbor search
World Wide Web, 2016The problem of kNN (k Nearest Neighbor) queries has received considerable attention in the database and information retrieval communities. Given a dataset D and a kNN query q, the k nearest neighbor algorithm finds the closest k data points to q. The applications of kNN queries are board, not only in spatio-temporal databases but also in many areas ...
Yu-Chi Chung +3 more
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2013
This chapter gives an introduction to pattern recognition and machine learning via K-nearest neighbors. Nearest neighbor methods will have an important part to play in this book. The chapter starts with an introduction to foundations in machine learning and decision theory with a focus on classification and regression.
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This chapter gives an introduction to pattern recognition and machine learning via K-nearest neighbors. Nearest neighbor methods will have an important part to play in this book. The chapter starts with an introduction to foundations in machine learning and decision theory with a focus on classification and regression.
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Generalized k-nearest neighbor rules
Fuzzy Sets and Systems, 1986This paper discusses a suitable framework for generalizing the k-nearest neighbor (k-NNR) algorithms to cases where the design labels are not necessarily crisp, i.e., not binary-valued. The proposed framework imbeds all crisp k-NNR's into a larger structure of fuzzy k-NNR's. The resultant model enables neighborhood voting to be a continuous function of
Bezdek, James C. +2 more
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K-Nearest Neighbor Finding Using MaxNearestDist
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008Similarity searching often reduces to finding the k nearest neighbors to a query object. Finding the k nearest neighbors is achieved by applying either a depth- first or a best-first algorithm to the search hierarchy containing the data. These algorithms are generally applicable to any index based on hierarchical clustering.
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k-Nearest Neighbor Prediction Functions
2016The purpose of the k-nearest neighbor prediction function is to predict a target variable from a predictor vector. Commonly, the target is a categorical variable, a label identifying the group from which the observation was drawn. The analyst has no knowledge of the membership label but does have the information coded in the attributes of the predictor
Brian Steele +2 more
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