In this paper, a novel EPA-KNN (Emerging Patterns Advanced-K Nearest Neighbors) gene classification algorithm is proposed. Bayes estimation is applied for the ...
A Novel EPA-KNN Gene Classification Algorithm. 1255. In this paper, we propose a novel EPA-KNN gene classification algorithm. In which, Bayes estimation is ...
In this paper, a novel EPA-KNN (Emerging Patterns Advanced-K Nearest Neighbors) gene classification algorithm is proposed. Bayes estimation is applied for the ...
This paper proposes a new classification technique for gene expression data, which is called Modified k-nearest neighbor (MKNN).
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This process starts with mapping the training dataset onto a one-dimensional distance space based on the calculated similarities, and then labeling the query in ...
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The GA/KNN selects the most discriminative variables for sample classification. It can be used for analysis of microarray gene expression data, proteomic data.
The K-NN algorithm is positioned under the supervised type learning technique and is considered one of the easiest-to-use algorithms in Machine Learning.
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Aug 11, 2024 · This paper presents a comprehensive review and performance analysis of modifications made to enhance the exact kNN techniques.
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The weighted KNN graphs can then be used to calculate distance and natural neighbor efficiently for a given test sample, so during the progress of generalized ...
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KNNHI: Resilient KNN algorithm for heterogeneous incomplete data ...
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Jan 11, 2022 · The original K-nearest neighbour (KNN) algorithm was meant to classify homogeneous complete data, that is, data with only numerical features ...