Improved Ensemble Methods to Solve Multi-class Imbalance Problem Using Adaptive Weights

Kokilam, K. Vasantha; Latha, D. Ponmary Pushpa

doi:10.1007/978-981-10-6319-0_28

Lecture Notes on Data Engineering and Communications Technologies

2017

DOI: 10.1007/978-981-10-6319-0_28

|View full text |Cite

Improved Ensemble Methods to Solve Multi-class Imbalance Problem Using Adaptive Weights

K. Vasantha Kokilam

D. Ponmary Pushpa Latha

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2020

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Ensemble Method to classify multi class with concept drift

Kokilam

Latha

2020

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

Real world scenarios is expected to have more number of classes, evolution of new class, change in concept, different distribution of data etc., This paper focus on the joint problem of multi class imbalance problem and different types of concept drift detection. The proposed work Enhanced Concept drift based Resampling ensemble (ECDRE) algorithm addresses the combined issues of Multi Class Imbalance problem with different types of Concept Drifts using the Ensemble Relearning Model. In this framework, EMUOB and EMOOB algorithm helps to re-balance the multi class distribution of the latest block. Window based strategy is used to classify the multi class imbalance dataset with different types of drifts. A group of classifiers form an ensemble and members belonging to the ensembles are updated frequently, where weights are calculated to emphasis misclassification cost. Meanwhile, ECDRE is swiftly adaptable to new conditions. Observed studies proof that ECDRE algorithm is more effective for learning non-stationary environment which suffers from multi class imbalance with different types of concept drift. CDRE algorithm was implemented to deal with multi-class problem and concept drift, but it is not much efficient to handle all types of concept drift. Even in situations of Data Streams which suffer from drifts such as real, virtual and recurring types, ECDRE works well and shows overall better results of above 80% accuracy.

show abstract

Ensemble Method to classify multi class with concept drift

Kokilam

Latha

2020

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Improved Ensemble Methods to Solve Multi-class Imbalance Problem Using Adaptive Weights

Cited by 1 publication

References 21 publications

Ensemble Method to classify multi class with concept drift

Ensemble Method to classify multi class with concept drift

Contact Info

Product

Resources

About