Text Categorization Using Weight Adjusted k-Nearest Neighbor Classification

By Eui-Hong Sam Han, George Karypis, Vipin Kumar. Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2001

496 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/b3ca32cafe5343a7602549ae5e51fc2660633cbf

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Citations in the corpus

Centroid-Based Document Classification: Analysis and Experimental Results (2000, methodology): “The various document categorization algorithms that have been developed over the years [47, 1, 10, 17, 31, 22, 25, 2, 55, 26, 19 ] fall under two general categories.”
A Brief Review of Nearest Neighbor Algorithm for Learning and Classification (2019, methodology): “The algorithm by [2] suggests that the distances, on which the search for the nearest neighbours is based in the first step, have to be transformed into similar measures, which can be used as weights.”
Text Classification Algorithms: A Survey (2019, background): “Weight Adjusted k-Nearest Neighbor Classification The Weight Adjusted k-Nearest Neighbor Classification (WAKNN) is a version of k-NN which tries to learn the weight vectors for classification [137].”
Survey on supervised machine learning techniques for automatic text classification (2019, methodology): “They found that the SVM technique outperformed the results of classification Naïve Bayes of both title and text parts was applied by Hassan et al. (2011). On the other hand, utilizing of meta-features in automatic document classification has allowed significant enhancements in the efficiency of clas”
Explaining Data-Driven Document Classifications (2014, methodology): “If such a technique is used as classification method, see, e.g. D’Silva et al. (2011), Han et al. (2001), showing theseK nearest neighbors and their classes “explains” why the model had chosen that classification.”
A Brief Survey of Text Mining: Classification, Clustering and Extraction Techniques (2017, background): “as the class label, see [61, 93, 117, 126] for more information and examples.”
Top 10 algorithms in data mining (2007, background): “A number of schemes have been developed that try to compute the weights of each individual attribute based upon a training set [32].”
A Review of Machine Learning Algorithms for Text-Documents Classification (2010, background): “Word Sense How we clarify the meaning of the word in the text, ambiguity problem.”
A Survey of Text Classification Algorithms (2012, methodology): “Some examples of such methods are discussed in [25, 54, 134].”
Deep feature weighting for naive Bayes and its application to text classification (2016, background): “Text classification presents unique challenges due to large number of features, large number of documents and strong dependencies among features (Han and Karypis, 2000; Han et al., 2001).”
Supervised and Traditional Term Weighting Methods for Automatic Text Categorization (2009, unknown): “Nevertheless, there is another approach that combines the term weighting method with a text classifier [5].”
Text feature extraction based on deep learning: a review (2017, unknown): “Han [30] put forward a kind of combination of KNN classifier weighted feature extraction problem.”