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Fundamenta Informaticae, 2021
The k Nearest Neighbor (KNN) algorithm has been widely applied in various supervised learning tasks due to its simplicity and effectiveness. However, the quality of KNN decision making is directly affected by the quality of the neighborhoods in the modeling space.
Linh Le +2 more
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The k Nearest Neighbor (KNN) algorithm has been widely applied in various supervised learning tasks due to its simplicity and effectiveness. However, the quality of KNN decision making is directly affected by the quality of the neighborhoods in the modeling space.
Linh Le +2 more
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kNN-P: A kNN classifier optimized by P systems
Theoretical Computer Science, 2020zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Juan Hu +3 more
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Learning k for kNN Classification
ACM Transactions on Intelligent Systems and Technology, 2017 Link to a related website: http://dl.acm.org/ft_gateway.cfm?id=2990508&type=pdf, Open Access via UnpaywallThe K Nearest Neighbor (kNN) method has widely been used in the applications of data mining andmachine learning due to its simple implementation and
Shichao Zhang, Xuelong Li, Ming Zong
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An Improved kNN Algorithm – Fuzzy kNN
2005As a simple, effective and nonparametric classification method, kNN algorithm is widely used in text classification. However, there is an obvious problem: when the density of training data is uneven it may decrease the precision of classification if we only consider the sequence of first k nearest neighbors but do not consider the differences of ...
Wenqian Shang +5 more
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Proceedings of the 4th International Conference on Communication and Information Processing, 2018
K nearest neighbor (kNN) method is a popular classification method in data mining because of its simple implementation and significant classification performance. However, kNN do not scale well to big datasets. In this paper, CLUKER, a novel kNN regression method based on hierarchical clustering, is proposed.
Yi Xiang +3 more
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K nearest neighbor (kNN) method is a popular classification method in data mining because of its simple implementation and significant classification performance. However, kNN do not scale well to big datasets. In this paper, CLUKER, a novel kNN regression method based on hierarchical clustering, is proposed.
Yi Xiang +3 more
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Proceedings of the 2008 ACM conference on Recommender systems, 2008
Recommender systems, based on collaborative filtering, draw their strength from techniques that manipulate a set of user-rating profiles in order to compute predicted ratings of unrated items. There are a wide range of techniques that can be applied to this problem; however, the k-nearest neighbour (kNN) algorithm has become the dominant method used in
Neal Lathia, Stephen Hailes, Licia Capra
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Recommender systems, based on collaborative filtering, draw their strength from techniques that manipulate a set of user-rating profiles in order to compute predicted ratings of unrated items. There are a wide range of techniques that can be applied to this problem; however, the k-nearest neighbour (kNN) algorithm has become the dominant method used in
Neal Lathia, Stephen Hailes, Licia Capra
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Efficient Classification with Adaptive KNN
Proceedings of the AAAI Conference on Artificial Intelligence, 2021In this paper, we propose an adaptive kNN method for classification, in which different k are selected for different test samples. Our selection rule is easy to implement since it is completely adaptive and does not require any knowledge of the underlying distribution. The convergence rate of the risk of this classifier to the Bayes risk is shown to be
Puning Zhao, Lifeng Lai
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Proceedings of the 2021 International Conference on Management of Data, 2021
kNN join over data streams is an important operation for location-aware systems, which correlates events from different sources based on their occurrence locations. Combining the complexity of kNN join and the dynamicity of data streams, kNN join in streaming environments is a computationally intensive operator, and its performance can be greatly ...
Amirhesam Shahvarani, Hans-Arno Jacobsen
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kNN join over data streams is an important operation for location-aware systems, which correlates events from different sources based on their occurrence locations. Combining the complexity of kNN join and the dynamicity of data streams, kNN join in streaming environments is a computationally intensive operator, and its performance can be greatly ...
Amirhesam Shahvarani, Hans-Arno Jacobsen
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2016 IEEE High Performance Extreme Computing Conference (HPEC), 2016
The first step of the K-nearest neighbor classification is to find the K-nearest neighbors of the query. A basic operation in calculating Jaccard distance is to count the number of ones in a binary vector - population count. This article focuses on finding the K-nearest neighbors in a high-dimensional Jaccard space.
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The first step of the K-nearest neighbor classification is to find the K-nearest neighbors of the query. A basic operation in calculating Jaccard distance is to count the number of ones in a binary vector - population count. This article focuses on finding the K-nearest neighbors in a high-dimensional Jaccard space.
openaire +1 more source