NDLI: Adapt Bagging to Nearest Neighbor Classifiers

Content Provider	Springer Nature Link
Author	Zhou, Zhi Hua Yu, Yang
Copyright Year	2005
Abstract	It is well-known that in order to build a strong ensemble, the component learners should be with high diversity as well as high accuracy. If perturbing the training set can cause significant changes in the component learners constructed, then Bagging can effectively improve accuracy. However, for stable learners such as nearest neighbor classifiers, perturbing the training set can hardly produce diverse component learners, therefore Bagging does not work well. This paper adapts Bagging to nearest neighbor classifiers through injecting randomness to distance metrics. In constructing the component learners, both the training set and the distance metric employed for identifying the neighbors are perturbed. A large scale empirical study reported in this paper shows that the proposed BagInRand algorithm can effectively improve the accuracy of nearest neighbor classifiers.
Starting Page	48
Ending Page	54
Page Count	7
File Format	PDF
ISSN	10009000
Journal	Journal of Computer Science and Technology
Volume Number	20
Issue Number	1
e-ISSN	18604749
Language	English
Publisher	Kluwer Academic Publishers
Publisher Date	2005-01-01
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	bagging data mining ensemble learning machine learning Minkowsky distance nearest neighbor value difference metric Computer Science Software Engineering Data Structures, Cryptology and Information Theory Theory of Computation Information Systems Applications (incl.Internet) Artificial Intelligence (incl. Robotics)
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Computational Theory and Mathematics Computer Science Applications Software Hardware and Architecture

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

Composite Distance Transformation for Indexing and k-Nearest-Neighbor Searching in High-Dimensional Spaces

Efficient Metric All-k-Nearest-Neighbor Search on Datasets Without Any Index

Combining Committee-Based Semi-Supervised Learning and Active Learning

Optimal selection of reference set for the nearest neighbor classification by Tabu search

A Hybrid Instance Selection Using Nearest-Neighbor for Cross-Project Defect Prediction

Efficient k-Nearest-Neighbor Search Algorithms for Historical Moving Object Trajectories

Discriminative Histogram Intersection Metric Learning and Its Applications

A New Approach for Multi-Document Update Summarization

Multi-Instance Learning from Supervised View

Adapt Bagging to Nearest Neighbor Classifiers

Similar Documents

Composite Distance Transformation for Indexing and k-Nearest-Neighbor Searching in High-Dimensional Spaces

Efficient Metric All-k-Nearest-Neighbor Search on Datasets Without Any Index

Combining Committee-Based Semi-Supervised Learning and Active Learning

Optimal selection of reference set for the nearest neighbor classification by Tabu search

A Hybrid Instance Selection Using Nearest-Neighbor for Cross-Project Defect Prediction

Efficient k-Nearest-Neighbor Search Algorithms for Historical Moving Object Trajectories

Discriminative Histogram Intersection Metric Learning and Its Applications

A New Approach for Multi-Document Update Summarization

Multi-Instance Learning from Supervised View

Adapt Bagging to Nearest Neighbor Classifiers