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Cost-sensitive KNN classification

Neurocomputing, 2020
Abstract KNN (K Nearest Neighbors) classification is one of top-10 data mining algorithms. It is significant to extend KNN classifiers sensitive to costs for imbalanced data classification applications. This paper designs two efficient cost-sensitive KNN classification models, referred to Direct-CS-KNN classifier and Distance-CS-KNN classifier.
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Regelungstechnische Anwendungen von KNN

1998
Die ersten Beispiele von Anwendungen Kunstlicher Neuronaler Netze (KNN) in der Regelungstechnik wurden bereits Mitte der sechziger Jahre bekannt.
Serge Zakharian   +2 more
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???????????? ???? ?????????? ???????????? ?????????????? ???????????? ???????????????????????? kNN ????????????????????????????

2010
???????????????????????? ???????????? ???????????????????? ?????????????? ???????????? ?????????????????????? ??????????????????????????, ???? ?????? ???????????????????? ?????????????????????????????? ???? ?????? ?????????? ???????????????? ?????????? ??????????????. ???????????? ???????????????????? ???????????????????? ?????????????????? ????????????
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Einsatz von KNN

1998
Die Feststellung von Rehkugler & Zimmermann (1994), das die ... Anzahl der empirischen Studien am Aktienmarkt wie auch auf anderen Kapitalmarkten, die dem Problem des Overlearning mittels der dargestellten Optimierungsmethodik Rechnung tragen, [...] bis dato gering ist, last sich damit begrunden, das es allgemein keine empirische Validierung von KNN ...
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Simulationsinstrumente für KNN

1998
KNN lassen sich auf Hardware- oder Software-Basis simulieren. Fur die Hardwaresimulation konnen eine Reihe von Computerarchitekturen verwendet werden. Zu diesen Computerklassen gehoren beispielsweise SIMD- und MIMD-Parallelrechner, die aus mehreren einfachen Prozessoren bestehen und parallel die gleichen Daten verarbeiten.
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Genetically Evolved kNN Ensembles

2009
Both theory and a wealth of empirical studies have established that ensembles are more accurate than single predictive models. For the ensemble approach to work, base classifiers must not only be accurate but also diverse, i.e., they should commit their errors on different instances.
Ulf Johansson   +2 more
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RSSI-KNN: A RSSI Indoor Localization Approach with KNN

2023 IEEE 2nd International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA), 2023
Xiaoshan Zheng   +2 more
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Revolutionizing Recruitment: A Comparative Study Of KNN, Weighted KNN, and SVM - KNN for Resume Screening

2023 8th International Conference on Communication and Electronics Systems (ICCES), 2023
Rishabh Bathija   +4 more
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Random KNN

2014 IEEE International Conference on Data Mining Workshop, 2014
Shengqiao Li   +2 more
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kNN

2009
Michael Steinbach, Pang-Ning Tan
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