A fuzzy K-nearest neighbor algorithm

By James M. Keller, Michael R. Gray, James A. Givens. 1985

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Citations in the corpus

Inductive Logic Programming: Theory and Methods (1994, background): “• K-Nearest Neighbours: classification by majority-vote of an objects direct neighbors, depending on similarity of the feature-values[8].”
Hybrid and ensemble-based soft computing techniques in bankruptcy prediction: a survey (2010, methodology): “Bian and Mazlack (2003) proposed combining the fuzzy k-nearest neighbour algorithm (Keller et al. 1985) and the rough sets theory, to improve the accuracy of bankruptcy prediction.”
Knowledge-based Collaborative Deep Learning for Benign-Malignant Lung Nodule Classification on Chest CT (2019, methodology): “The extracted visual features can then be sued to train a classiﬁer, such as the support vector machine (SVM) [20], K-nearest neighbor (KNN) [21], back propagation neural network (BPNN) [22], [23], and random forest [24].”
Machine learning approaches and databases for prediction of drug–target interaction: a survey paper (2020, methodology): “In the predictor, the drug compound is formulated by a 2D fingerprint via a 256D vector, GPCRs by the pseudo amino acid composition [ 226] generated with the gray model theory and the prediction engine is operated by the fuzzy K-nearest neighbor (KNN) classification method [ 227].”
State-of-the-art in artificial neural network applications: A survey (2018, background): “Also, popular supervised ANNs include support vector machines (SVMs) [46, 47], k-nearest neighbours (kNNs) [48].”
Machine Learning Algorithms: A Review (2022, unknown): “Naïve Bayes is another classification based machine learning approach which involves mainly the conditional probability method to determine whether the object belongs to particular class or not.”
Medical Image Segmentation Methods, Algorithms, and Applications (2014, background): “In fuzzy k-nn , each datum belongs to a class with some weights [61].”
On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice (2020, background): “Assuming the training set T = {(x1, y1), (x2, y2), · · · , (xn, yn)}, xi is the feature vector of an instance, and yi ∈ {c1, c2, · · · , cm} is the class of the instance, i = (1, 2, · · ·n), for a test instance x, its class y can be denoted by [41]:”