NDLI: Resampling and Cost-Sensitive Methods for Imbalanced Multi-instance Learning

Content Provider	IEEE Xplore Digital Library
Author	Xiaoguang Wang Xuan Liu Japkowicz, N. Matwin, S.
Copyright Year	2013
Description	Author affiliation: Sch. of Electr. Eng. & Comput. Sci., Univ. of Ottawa, Ottawa, ON, Canada (Xiaoguang Wang; Xuan Liu; Japkowicz, N.) \|\| Fac. of Comput. Sci., Dalhousie Univ., Halifax, NS, Canada (Matwin, S.)
Abstract	Multi-instance learning uses a set of bags containing many instances, which makes it different from standard propositional classification. Our research shows that, similar to the single-instance imbalance problem, classification of multi-instance data with imbalanced class distributions significantly degrades performance when compared to most standard multi-instance algorithms in a balanced setting. Due to the inherent differences between multi-instance and single-instance learning, the existing solutions for single-instance class imbalance problems do not transfer directly to multi-instance datasets. This is a drawback, as imbalanced multi-instance problems often occur in data mining practice. In this paper, we propose two solution frameworks for multi-instance class imbalanced datasets. In the first we explore multi-instance data sampling methods, and in the second we present a novel generalized version of a multi-instance cost-sensitive boosting technique. Experimental results, on benchmark datasets and application datasets, show that the proposed frameworks are an effective solution for the multi-instance class imbalance problem.
Sponsorship	Toshiba
Starting Page	808
Ending Page	816
File Size	651695
Page Count	9
File Format	PDF
e-ISBN	9781479931422
DOI	10.1109/ICDMW.2013.85
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Class Imbalance Boosting Sampling methods Educational institutions Multi-instance learning Data mining Sonar detection
Content Type	Text
Resource Type	Article

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

Sampled Bayesian Network Classifiers for Class-Imbalance and Cost-Sensitive Learning

Exploratory Under-Sampling for Class-Imbalance Learning

A Comparative Study of Data Sampling and Cost Sensitive Learning

Graph classification with imbalanced data sets

Learning from combination of data chunks for multi-class imbalanced data

Ensemble of Cost-Sensitive Hypernetworks for Class-Imbalance Learning

Generative Oversampling for Mining Imbalanced Datasets

A Dynamic Sampling Framework for Multi-class Imbalanced Data

Boosting for learning multiple classes with imbalanced class distribution (2006)

Resampling and Cost-Sensitive Methods for Imbalanced Multi-instance Learning

Similar Documents

Sampled Bayesian Network Classifiers for Class-Imbalance and Cost-Sensitive Learning

Exploratory Under-Sampling for Class-Imbalance Learning

A Comparative Study of Data Sampling and Cost Sensitive Learning

Graph classification with imbalanced data sets

Learning from combination of data chunks for multi-class imbalanced data

Ensemble of Cost-Sensitive Hypernetworks for Class-Imbalance Learning

Generative Oversampling for Mining Imbalanced Datasets

A Dynamic Sampling Framework for Multi-class Imbalanced Data

Boosting for learning multiple classes with imbalanced class distribution (2006)

Resampling and Cost-Sensitive Methods for Imbalanced Multi-instance Learning