Improving kNN Text Categorization by Removing Outliers from Training Set

Shin, Kwangcheol; Abraham, Ajith; Han, Sang Yong

doi:10.1007/11671299_58

Cited by 21 publications

(12 citation statements)

References 6 publications

(7 reference statements)

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“…Many of them focus on reducing classification time [3,4]. Other algorithms focus on increasing classification rates, either changing the method to find nearest neighbors [5], varying the voting schema [6] or improving the training data [7].…”

Section: Introductionmentioning

confidence: 99%

A New Nearest Neighbor Rule for Text Categorization

Gil-García¹,

Pons-Porrata²

2006

Lecture Notes in Computer Science

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The nearest neighbor (NN) rule is usually chosen in a large number of pattern recognition systems due to its simplicity and good properties. In particular, this rule has been successfully applied to text categorization. A vast number of NN algorithms have been developed during the last years. They differ in how they find the nearest neighbors, how they obtain the votes of categories, and which decision rule they use. A new NN classification rule which comes from the use of a different definition of neighborhood is introduced in this paper. The experimental results on Reuters-21578 standard benchmark collection show that our algorithm achieves better classification rates than the k-NN rule while decreasing classification time.

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Section: Introductionmentioning

confidence: 99%

A New Nearest Neighbor Rule for Text Categorization

Gil-García¹,

Pons-Porrata²

2006

Lecture Notes in Computer Science

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“…to construct a cross-lingual feature space and uniformly represent different language texts. Secondly, they use traditional monolingual text classification methods to classify, e.g., K-nearest Neighbor [Shin, Abraham and Han (2006)], Naive Bayes [Kim, Han, Rim et al (2006)], Support Vector Machines [Martens, Huysmans, Setiono et al (2008)] and so on. The main difference between different methods is the construction of cross-lingual feature space.…”

Section: Related Workmentioning

confidence: 99%

Cross-Lingual Non-ferrous Metals Related News Recognition Method Based on CNN with a Limited Bi-lingual Dictionary

Hong

Xia²,

Wei³

et al. 2019

Computers, Materials &Amp; Continua

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To acquire non-ferrous metals related news from different countries' internet, we proposed a cross-lingual non-ferrous metals related news recognition method based on CNN with a limited bilingual dictionary. Firstly, considering the lack of related language resources of non-ferrous metals, we use a limited bilingual dictionary and CCA to learn cross-lingual word vector and to represent news in different languages uniformly. Then, to improve the effect of recognition, we use a variant of the CNN to learn recognition features and construct the recognition model. The experimental results show that our proposed method acquires better results.

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“…• k-NN classifier is noise tolerant since it uses all training data as relevant, even when training documents contain noise or unbalanced data [28,37].…”

Section: K-nn Improvements For Tcmentioning

confidence: 99%

Improving K-Nearest Neighbor Efficiency for Text Categorization

Barigou¹

2016

NNW

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Precise wind energy potential assessment is vital for wind energy generation and planning and development of new wind power plants. This work proposes and evaluates a novel two-stage method for location-specific wind energy potential assessment. It combines accurate statistical modelling of annual wind direction distribution in a given location with supervised machine learning of efficient estimators that can approximate energy efficiency coefficients from the parameters of optimized statistical wind direction models. The statistical models are optimized using differential evolution and energy efficiency is approximated by evolutionary fuzzy rules.

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Improving kNN Text Categorization by Removing Outliers from Training Set

Cited by 21 publications

References 6 publications

A New Nearest Neighbor Rule for Text Categorization

A New Nearest Neighbor Rule for Text Categorization

Cross-Lingual Non-ferrous Metals Related News Recognition Method Based on CNN with a Limited Bi-lingual Dictionary

Improving K-Nearest Neighbor Efficiency for Text Categorization

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