DocumentCode
530222
Title
Notice of Retraction
The application of improved K-Nearest Neighbor classification in topic tracking
Author
Hongxiang Diao ; Zhansheng Bai ; Xilin Yu
Author_Institution
Inf. Sci. & Technol. Inst., Hunan Agric. Univ., Changsha, China
Volume
2
fYear
2010
fDate
17-19 Sept. 2010
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
News topic tracking is one of the major tasks of TDT, the aim of which is monitoring news story flow as well as recognizing and giving subsequent stories in advance related to topics described by several news stories. This paper explains in details the concept of topic tracking and the common methods at present; aiming at the scarcity of positive example of training, this paper makes effective improvement on traditional KNN taxonomy and applies it to topic tracking; besides, it adds time window strategy to the process of topic tracking, which effectively reduces calculation complexity. The final experimental result also proves that this method is superior to traditional KNN topic tracking method.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
News topic tracking is one of the major tasks of TDT, the aim of which is monitoring news story flow as well as recognizing and giving subsequent stories in advance related to topics described by several news stories. This paper explains in details the concept of topic tracking and the common methods at present; aiming at the scarcity of positive example of training, this paper makes effective improvement on traditional KNN taxonomy and applies it to topic tracking; besides, it adds time window strategy to the process of topic tracking, which effectively reduces calculation complexity. The final experimental result also proves that this method is superior to traditional KNN topic tracking method.
Keywords
pattern classification; publishing; text analysis; KNN taxonomy; calculation complexity; k-nearest neighbor classification; news story flow; news topic tracking; text categorization; time window strategy; training example; KNN; Text Categorization; Topic Tracking;
fLanguage
English
Publisher
ieee
Conference_Titel
Educational and Information Technology (ICEIT), 2010 International Conference on
Conference_Location
Chongqing
Print_ISBN
978-1-4244-8033-3
Type
conf
DOI
10.1109/ICEIT.2010.5607527
Filename
5607527
Link To Document