A reduced labeled samples (RLS) framework for classification of imbalanced concept-drifting streaming data.

Arabmakki, Elaheh

doi:10.18297/etd/2602

DOI: 10.18297/etd/2602

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A reduced labeled samples (RLS) framework for classification of imbalanced concept-drifting streaming data.

Elaheh Arabmakki¹

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A Survey of Class Imbalance Problem on Evolving Data Stream

Himaja

Padmaja²,

Krishna

2021

Data Preprocessing, Active Learning, and Cost Perceptive Approaches for Resolving Data Imbalance

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Learning from data streams with both online class imbalance and concept drift (OCI-CD) is receiving much attention in today's world. Due to this problem, the performance is affected for the current models that learn from both stationary as well as non-stationary environments. In the case of non-stationary environments, due to the imbalance, it is hard to spot the concept drift using conventional drift detection methods that aim at tracking the change detection based on the learner's performance. There is limited work on the combined problem from imbalanced evolving streams both from stationary and non-stationary environments. Here the data may be evolved with complete labels or with only limited labels. This chapter's main emphasis is to provide different methods for the purpose of resolving the issue of class imbalance in emerging streams, which involves changing and unchanging environments with supervised and availability of limited labels.

show abstract

A Survey of Class Imbalance Problem on Evolving Data Stream

Himaja

Padmaja²,

Krishna

2021

Data Preprocessing, Active Learning, and Cost Perceptive Approaches for Resolving Data Imbalance

View full text Add to dashboard Cite

show abstract

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A reduced labeled samples (RLS) framework for classification of imbalanced concept-drifting streaming data.

Cited by 1 publication

References 58 publications

A Survey of Class Imbalance Problem on Evolving Data Stream

A Survey of Class Imbalance Problem on Evolving Data Stream

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