KNN Model-Based Approach in Classification

By Gongde Guo, Hui Wang, David A. Bell, Yaxin Bi, Kieran R. C. Greer. OTM Conferences / Workshops, 2003

1,970 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/a7e2814ec5db800d2f8c4313fd436e9cf8273821

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

Citations in the corpus

Audio-Visual Model Distillation Using Acoustic Images (2019, methodology): “Section 5 are fed into a k-NN [16] classifier.”
Efficient kNN Classification With Different Numbers of Nearest Neighbors (2018, methodology): “manifold learning methods [50], [57], [58].”
Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence (2023, background): “The explanation is based on the principle that the algorithm classifies a new data point based on the class labels of its nearest neighbos in the training dataset [125].”
Machine learning: a review of classification and combining techniques (2006, methodology): “…such as: (i) they have large storage requirements , (ii) they are sensitive to the choice of the similarity function that is used to compare instances, (iii) they lack a principled way to choose k, except through cross-validation or similar, computationally-expensive technique (Guo et al. 2003).”
TEXT CLASSIFICATION AND CLASSIFIERS: A SURVEY (2012, methodology): “K-NN classifier is a case-based learning [9] algorithm that is based on a distance or similarity function for pairs of observations, such as the Euclidean distance or Cosine similarity measure’s This method is try for many application [10] Because of its effectiveness, non-parametric and easy to imp”
Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach: Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier (2020, background): “Based on an empirical rule-of-thumb introduced by Dude [110,111], K is equal to the square root of the number of samples; this makes parameter tuning difficult for diverse applications.”
Supervised Machine Learning: A Review of Classification Techniques (2007, neutral): “…kNN, such as: i) they have large storage requirements, ii) they are sensitive to the choice of the similarity function that is used to compare instances, iii) they lack a principled way to choose k , except through cross-validation or similar, computationally-expensive technique (Guo et al. 2003).”
A theoretical distribution analysis of synthetic minority oversampling technique (SMOTE) for imbalanced learning (2023, neutral): “Standard machine learning classifiers such as Support vector machines (SVM) (Hearst et al., 1998), decision trees (Quinlan, 1996), and K-nearest neighbor (KNN) (Guo et al., 2003) generally assume at least implicitly an even class distribution.”