An improved K-nearest-neighbor algorithm for text categorization

By Sheng-yi Jiang, Guansong Pang, Meiling Wu, Limin Kuang. 2012

263 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Natural Language Processing

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

1. Francisco Herrera
2. Salvador Garcia
3. Ammar Ismael Kadhim
4. Daniel Molina
5. Javier Del Ser
6. Saeed Reza Aghabozorgi
7. Teh Ying Wah
8. Adrien Bennetot

In Information Systems

1. Salvador Garcia
2. Francisco Herrera
3. Teh Ying Wah
4. Laura E. Barnes
5. Arman Khadjeh Nassirtoussi
6. David Chek Ling Ngo
7. Kamran Kowsari
8. Donald E. Brown

In Natural Language Processing

1. Francisco Herrera
2. Teh Ying Wah
3. Laura E. Barnes
4. Arman Khadjeh Nassirtoussi
5. Natalia Díaz Rodríguez
6. David Chek Ling Ngo
7. Kamran Kowsari
8. Donald E. Brown

Citations in the corpus

• Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI (2019, positive): “There lies the rationale of why KNN has also been adopted widely in contexts in which model interpretability is a requirement [186–189] .”
• A Brief Review of Nearest Neighbor Algorithm for Learning and Classification (2019, methodology): “In [3], a refined KNN algorithm for text categorization, which constructs the classification model by merging KNN text categorization and restricted one pass clustering algorithm has been proposed.”
• A Survey on Text Classification: From Traditional to Deep Learning (2020, unknown): “and the amount of data, the KNN algorithm takes an unusually long time on the large-scale datasets [39].”
• Text Classification Algorithms: A Survey (2019, methodology): “After sorting the score values, the algorithm assigns the candidate to the class with the highest score from the test document x [136].”
• Survey on supervised machine learning techniques for automatic text classification (2019, background): “Several real life text data sets show the promise of WIND, as it is outperformed the state of the art categorization such as C4.5, VSM, RIPPER, Rainbow, and PEBBLES was proposed by Jiang et al. (2012).”
• Text mining of news-headlines for FOREX market prediction: A Multi-layer Dimension Reduction Algorithm with semantics and sentiment (2015, methodology): “There are many more examples of usage of neural networks for market-predictive technical analysis (Anastasakis & Mort, 2009; Ghazali, Hussain, & Liatsis, 2011; Vanstone & Finnie, 2010). Bahrepour, Akbarzadeh, Yaghoobi, and Naghibi (2011) have introduced an adaptive ordered fuzzy time series with app”