Data reduction techniques for highly imbalanced medicare Big Data

to related objects

Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: 1 Online-Ressource.

Language: Englisch

Bibliographic citation: Data reduction techniques for highly imbalanced medicare Big Data ; volume:11 ; number:1 ; day:3 ; month:1 ; year:2024 ; pages:1-41 ; date:12.2024
Journal of Big Data ; 11, Heft 1 (3.1.2024), 1-41, 12.2024

Classification: Soziale Probleme, Sozialdienste, Versicherungen

Creator: Hancock, John T.
Wang, Huanjing
Khoshgoftaar, Taghi M.
Liang, Qianxin

Contributor: SpringerLink (Online service)

DOI: 10.1186/s40537-023-00869-3

URN: urn:nbn:de:101:1-2024031814380984266068

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 14.08.2025, 10:55 AM CEST

Data provider

This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.

Show original at data provider

Associated

Hancock, John T.
Wang, Huanjing
Khoshgoftaar, Taghi M.
Liang, Qianxin
SpringerLink (Online service)

Other Objects (12)

Evaluating classifier performance with highly imbalanced Big Data

Severely imbalanced Big Data challenges: investigating data sampling approaches

Big Data fraud detection using multiple medicare data sources

Iterative cleaning and learning of big highly-imbalanced fraud data using unsupervised learning

Smart Data Driven Decision Trees Ensemble Methodology for Imbalanced Big Data

Examining characteristics of predictive models with imbalanced big data

Enhancing credit card fraud detection: highly imbalanced data case

An insight into imbalanced Big Data classification: outcomes and challenges

Predicting disease risks from highly imbalanced data using random forest

Improved cost-sensitive representation of data for solving the imbalanced big data classification problem

Synthesizing class labels for highly imbalanced credit card fraud detection data

SOCR data dashboard: an integrated big data archive mashing medicare, labor, census and econometric information

Evaluating classifier performance with highly imbalanced Big Data

Severely imbalanced Big Data challenges: investigating data sampling approaches

Big Data fraud detection using multiple medicare data sources

Iterative cleaning and learning of big highly-imbalanced fraud data using unsupervised learning

Smart Data Driven Decision Trees Ensemble Methodology for Imbalanced Big Data

Examining characteristics of predictive models with imbalanced big data

Enhancing credit card fraud detection: highly imbalanced data case

An insight into imbalanced Big Data classification: outcomes and challenges

Predicting disease risks from highly imbalanced data using random forest

Improved cost-sensitive representation of data for solving the imbalanced big data classification problem

Synthesizing class labels for highly imbalanced credit card fraud detection data

SOCR data dashboard: an integrated big data archive mashing medicare, labor, census and econometric information

Evaluating classifier performance with highly imbalanced Big Data

Severely imbalanced Big Data challenges: investigating data sampling approaches

Big Data fraud detection using multiple medicare data sources

Iterative cleaning and learning of big highly-imbalanced fraud data using unsupervised learning

Smart Data Driven Decision Trees Ensemble Methodology for Imbalanced Big Data

Examining characteristics of predictive models with imbalanced big data

Enhancing credit card fraud detection: highly imbalanced data case

An insight into imbalanced Big Data classification: outcomes and challenges

Predicting disease risks from highly imbalanced data using random forest

Improved cost-sensitive representation of data for solving the imbalanced big data classification problem

Synthesizing class labels for highly imbalanced credit card fraud detection data

SOCR data dashboard: an integrated big data archive mashing medicare, labor, census and econometric information

Cultural heritage institutions wishing to register will find more information here.

Fields marked * need to be filled in.

Username*

Please enter your username

Email*

Please enter your email address

Please do not fill this field

First name

Last name

Password*

Please enter your password

Confirm password*

Please enter the same password

I have read the terms of use and the privacy policy for the collection of personal data and accept them. *

This field is required.

I would like to subscribe to the newsletter of the Deutsche Digitale Bibliothek. See newsletter subscription info.

Account created

Your "My DDB" account has been successfully created. Before you can log in to your account, you must click the confirmation link in the message we just sent to the email address you provided.

Data reduction techniques for highly imbalanced medicare Big Data

Object Details

References and Relationships

Classification and Topics

Contributors, Places and Time

Further information

Data provider

Associated

Other Objects (12)

Evaluating classifier performance with highly imbalanced Big Data

Severely imbalanced Big Data challenges: investigating data sampling approaches

Big Data fraud detection using multiple medicare data sources

Iterative cleaning and learning of big highly-imbalanced fraud data using unsupervised learning

Smart Data Driven Decision Trees Ensemble Methodology for Imbalanced Big Data

Examining characteristics of predictive models with imbalanced big data

Enhancing credit card fraud detection: highly imbalanced data case

An insight into imbalanced Big Data classification: outcomes and challenges

Predicting disease risks from highly imbalanced data using random forest

Improved cost-sensitive representation of data for solving the imbalanced big data classification problem

Synthesizing class labels for highly imbalanced credit card fraud detection data

SOCR data dashboard: an integrated big data archive mashing medicare, labor, census and econometric information

Evaluating classifier performance with highly imbalanced Big Data

Severely imbalanced Big Data challenges: investigating data sampling approaches

Big Data fraud detection using multiple medicare data sources

Iterative cleaning and learning of big highly-imbalanced fraud data using unsupervised learning

Smart Data Driven Decision Trees Ensemble Methodology for Imbalanced Big Data

Examining characteristics of predictive models with imbalanced big data

Enhancing credit card fraud detection: highly imbalanced data case

An insight into imbalanced Big Data classification: outcomes and challenges

Predicting disease risks from highly imbalanced data using random forest

Improved cost-sensitive representation of data for solving the imbalanced big data classification problem

Synthesizing class labels for highly imbalanced credit card fraud detection data

SOCR data dashboard: an integrated big data archive mashing medicare, labor, census and econometric information

Evaluating classifier performance with highly imbalanced Big Data

Severely imbalanced Big Data challenges: investigating data sampling approaches

Big Data fraud detection using multiple medicare data sources

Iterative cleaning and learning of big highly-imbalanced fraud data using unsupervised learning

Smart Data Driven Decision Trees Ensemble Methodology for Imbalanced Big Data

Examining characteristics of predictive models with imbalanced big data

Enhancing credit card fraud detection: highly imbalanced data case

An insight into imbalanced Big Data classification: outcomes and challenges

Predicting disease risks from highly imbalanced data using random forest

Improved cost-sensitive representation of data for solving the imbalanced big data classification problem

Synthesizing class labels for highly imbalanced credit card fraud detection data

SOCR data dashboard: an integrated big data archive mashing medicare, labor, census and econometric information

Related objects

Reset password