NDLI: Locally Adaptive Text Classification Based K-Nearest Neighbors

Content Provider	IEEE Xplore Digital Library
Author	Xiao-gao Yu Xiao-peng Yu
Copyright Year	2007
Description	Author affiliation: Dept. of Inf. Manage., Hubei Univ. of Econ., Wuhan (Xiao-gao Yu)
Abstract	Due to the exponential growth of documents on the Internet and the emergent need to organize them, the automated categorization of documents into predefined labels has received an ever-increased attention in the recent years. Among all these classifiers, k-nearest neighbors (KNNC) is a widely used classifier in text categorization community because of its simplicity and efficiency. However, KNNC still suffers from inductive biases or model misfits that result from its assumptions, such as the presumption that training data are evenly distributed among all categories. In this paper, we propose a new refinement strategy (LAKNNC) for the KNN classifier, which adopts sum-of-squared-error criterion to adaptively select the contributing part from these neighbors and classifies the input document in term of the disturbance degree which it brings to the kernel densities of these selected neighbors. The experimental results indicate that our algorithm LAKNNC is not sensitive to the parameter k and achieves significant classification performance improvement on imbalanced corpora.
Starting Page	5651
Ending Page	5654
File Size	134662
Page Count	4
File Format	PDF
ISBN	9781424413119
DOI	10.1109/WICOM.2007.1385
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-09-21
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Technology management Smoothing methods Text categorization Training data Robustness Information management Internet Kernel Nearest neighbor searches Testing
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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