GEV-NN: A deep neural network architecture for class imbalance problem in binary classification

Lkhagvadorj Munkhdalai; Tsendsuren Munkhdalai; Keun Ho Ryu

doi:10.1016/j.knosys.2020.105534

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GEV-NN: A deep neural network architecture for class imbalance problem in binary classification

Lkhagvadorj Munkhdalai Tsendsuren Munkhdalai Keun Ho Ryu

Year: 2020 Journal: Knowledge-Based Systems Vol: 194 Pages: 105534-105534 Publisher: Elsevier BV

DOI: 10.1016/j.knosys.2020.105534

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Keywords:

Computer science Component (thermodynamics) Artificial intelligence Binary classification Artificial neural network Class (philosophy) Binary number Deep learning Gumbel distribution Set (abstract data type) Architecture Encoder Machine learning Pattern recognition (psychology) Support vector machine Mathematics Extreme value theory Statistics

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Imbalanced Data Classification Techniques

Physical Sciences → Computer Science → Artificial Intelligence

Financial Distress and Bankruptcy Prediction

Social Sciences → Business, Management and Accounting → Accounting

Electricity Theft Detection Techniques

Physical Sciences → Engineering → Electrical and Electronic Engineering

GEV-NN: A deep neural network architecture for class imbalance problem in binary classification

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