Three novel cost-sensitive machine learning models for urban growth modelling

Three novel cost-sensitive machine learning models for urban growth modelling

This article addresses the class imbalance problem in urban gain modelling (UGM) of Tabriz and Isfahan megacities in Iran by proposing novel cost-sensitive machine learning models, namely cost-sensitive support vector machine (CSVM), random forest (CRF) and artificial neural network (CANN). Random s...

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Bibliographic Details
Main Authors: Mohammad Ahmadlou, Mohammad Karimi, Saad Sh. Sammen, Karam Alsafadi
Format: Article
Language:English
Published: Taylor & Francis Group 2024-01-01
Series:Geocarto International
Subjects:
Imbalance problem
cost-sensitive algorithms
urban gain modelling
random sampling
data mining
machine learning model
Online Access:https://www.tandfonline.com/doi/10.1080/10106049.2024.2353252
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