Neighbor-weighted K-nearest neighbor for unbalanced text corpus

By Songbo Tan. Expert systems with applications, 2005

380 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/5adfecae50e354b96562b29b70cf7d8ab5adaff3

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Citations in the corpus

Feature selection for text classification with Naïve Bayes (2009, neutral): “Among this kinds of methods, decision trees (Lewis & Ringuette, 1994), k-nearest neighbors (kNN) (Cover & Hart, 1967; Tan, 2005; Yang, 1997; Yang & chute, 1994), neural networks (Wiener, Pedersen, & Weigend, 1995), Naïve Bayes (Lewis, 1998; McCallum & Nigam, 1998) and support vector machines (SVM) (”
Local Manifold Learning-Based $k$ -Nearest-Neighbor for Hyperspectral Image Classification (2010, background): “The k NN classiﬁer is impacted by the problem of imbalanced data sets [30].”
Neighborhood classifiers (2008, methodology): “Therefore, some techniques used to speed up and improve K-NN (Hart, 1968; Tan, 2005; Zhang & Srihari, 2004) also can be introduced.”
A novel feature selection algorithm for text categorization (2007, methodology): “For example, k-nearest neighbor (kNN) (Cover & Hart, 1967; Yang, 1997; Yang & Lin, 1999; Tan, 2005), Naive Bayes (Lewis, 1998), decision tree (Lewis & Ringuette, 1994), support vector machines (SVM) (Joachims, 1998), linear least squares ﬁt, neural network, SWAP-1, and Roc-chio are all such kinds…”
On the effectiveness of preprocessing methods when dealing with different levels of class imbalance (2012, methodology): “Experiments over 17 real data sets using eight different classifiers, four resampling algorithms and four performance evaluation measures show that over-sampling the minority class consistently outperforms under-sampling the majority class when data sets are strongly imbalanced, whereas there are…”
An improved K-nearest-neighbor algorithm for text categorization (2012, methodology): “We use F1 and Macro-F1 (Tan, 2005; Lewis et al., 1996) to measure our algorithm performance.”
Optimal Thresholding of Classifiers to Maximize F1 Measure (2014, background): “In general, macro averaging increases the impact on final score of performance on rare labels, while per instance averaging increases the importance of performing well on each example [17].”
An improved particle swarm optimization for feature selection (2011, methodology): “It has been successfully applied in various domains, such as feature selection [9,11,17], text classification [25], intrusion detection [15], and medicine [24].”
A Survey on Text Classification: From Shallow to Deep Learning (2020, background): “The neighbor-weighted K-nearest neighbor (NWKNN) [106] is proposed to improve classification performance on the unbalanced corpora.”
A Survey on Text Classification: From Traditional to Deep Learning (2020, background): “The Neighbor-Weighted K-Nearest Neighbor (NWKNN) [41] is proposed to improve classification performance on the unbalanced corpora.”