NDLI: Editing training data for multi-label classification with the k-nearest neighbor rule

Content Provider	Springer Nature Link
Author	Kanj, Sawsan Abdallah, Fahed Denœux, Thierry Tout, Kifah
Copyright Year	2015
Abstract	Multi-label classification allows instances to belong to several classes at once. It has received significant attention in machine learning and has found many real-world applications in recent years, such as text categorization, automatic video annotation and functional genomics, resulting in the development of many multi-label classification methods. Based on labeled examples in the training dataset, a multi-labeled method extracts inherent information to output a function that predicts the labels of unlabeled data. Due to several problems, like errors in the input vectors or in their labels, this information may be wrong and might lead the multi-label algorithm to fail. In this paper, we propose a simple algorithm for overcoming these problems by editing the existing training dataset, and adapting the edited set with different multi-label classification methods. Evaluation on benchmark datasets demonstrates the usefulness and effectiveness of our approach.
Starting Page	145
Ending Page	161
Page Count	17
File Format	PDF
ISSN	14337541
Journal	Pattern Analysis & Applications
Volume Number	19
Issue Number	1
e-ISSN	1433755X
Language	English
Publisher	Springer London
Publisher Date	2015-02-07
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Classification Multi-label $$k$$ -Nearest neighbor Prototype selection Edition Pattern Recognition
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Vision and Pattern Recognition

Sl.	Authority	Responsibilities	Communication Details
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
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Editing training data for multi-label classification with the k-nearest neighbor rule

Prototype Selection for Nearest Neighbor Classification: Taxonomy and Empirical Study

Locally adaptive k parameter selection for nearest neighbor classifier: one nearest cluster

Prototype reduction using an artificial immune model

Hybrid genetic algorithm for dual selection

Data pre-processing through reward–punishment editing

Towards enriching the quality of k-nearest neighbor rule for document classification

On kernel difference-weighted k-nearest neighbor classification

DECISION BOUNDARY PRESERVING PROTOTYPE SELECTION FOR NEAREST NEIGHBOR CLASSIFICATION

Editing training data for multi-label classification with the k-nearest neighbor rule

Similar Documents

Editing training data for multi-label classification with the k-nearest neighbor rule

Prototype Selection for Nearest Neighbor Classification: Taxonomy and Empirical Study

Locally adaptive k parameter selection for nearest neighbor classifier: one nearest cluster

Prototype reduction using an artificial immune model

Hybrid genetic algorithm for dual selection

Data pre-processing through reward–punishment editing

Towards enriching the quality of k-nearest neighbor rule for document classification

On kernel difference-weighted k-nearest neighbor classification

DECISION BOUNDARY PRESERVING PROTOTYPE SELECTION FOR NEAREST NEIGHBOR CLASSIFICATION

Editing training data for multi-label classification with the k-nearest neighbor rule