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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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Heart Disease Prediction Using Extended KNN (E-KNN)
2021The WHO estimates that deaths due to heart disease are the number one cause worldwide, accounting for around 30% annually taking an estimated 1.5 crores who die due to this disease. In this study, an extension of KNN algorithm known as E-KNN is used and compares with the results of different machine learning methods such as K-Nearest Neighbor (KNN ...
R. Sateesh Kumar, S. Sameen Fatima
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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.
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2021
In this chapter, you will discover the kNN model. The kNN model is the third supervised machine learning model that is covered in this book. Like the two previous models, the kNN model is also one of the simpler models. It is also intuitively easy to understand how the model works.
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In this chapter, you will discover the kNN model. The kNN model is the third supervised machine learning model that is covered in this book. Like the two previous models, the kNN model is also one of the simpler models. It is also intuitively easy to understand how the model works.
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2015
In this work, we incorporate new edges from a paraclique-identification approach to the output of the MST-kNN graph partitioning method. We present a statistical analysis of the results on a dataset originated from a computational linguistic study of 84 Indo-European languages.
Ahmed Shamsul Arefin +3 more
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In this work, we incorporate new edges from a paraclique-identification approach to the output of the MST-kNN graph partitioning method. We present a statistical analysis of the results on a dataset originated from a computational linguistic study of 84 Indo-European languages.
Ahmed Shamsul Arefin +3 more
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Analysis and evaluation of V*-kNN: an efficient algorithm for moving kNN queries
The VLDB Journal, 2009The moving k nearest neighbor (MkNN) query continuously finds the k nearest neighbors of a moving query point. MkNN queries can be efficiently processed through the use of safe regions. In general, a safe region is a region within which the query point can move without changing the query answer.
Sarana Nutanong +3 more
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Proceedings of the 30th Annual ACM Symposium on Applied Computing, 2015
In nowadays we observe that there is more data than that can be effectively analyzed. Organizing this data has become one of the biggest problems in Computer Science. Many algorithms have been proposed for this purpose, highlighting those related to the Data Mining area, specifically the automatic document classification (ADC) algorithms.
Leonardo Rocha 0001 +9 more
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In nowadays we observe that there is more data than that can be effectively analyzed. Organizing this data has become one of the biggest problems in Computer Science. Many algorithms have been proposed for this purpose, highlighting those related to the Data Mining area, specifically the automatic document classification (ADC) algorithms.
Leonardo Rocha 0001 +9 more
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Classification of Gunshots with KNN Classifier
Proceedings of the Euro American Conference on Telematics and Information Systems, 2018In this article a system of detection and classification of gunshots is proposed, which consists of using the KNN classifier in the presence and absence of Gaussian additive noise. The results guarantee that the classifier reaches up to 94 % of performance in the absence of noise and only using 10 attributes. The attributes proposed in this article are
Francisco D. Pichardo-Morales +2 more
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A granular, parametric KNN classifier
Proceedings of the 17th Panhellenic Conference on Informatics, 2013This work presents a granular K Nearest Neighbor, or grKNN for short, classifier in the metric lattice of Intervals' Numbers (INs). An IN here represents a population of numeric data samples. We detail how the grKNN classifier can be parameterized towards optimizing it.
Vassilis Tsoukalas +2 more
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