Classification of imbalanced hyperspectral images using SMOTE-based deep learning methods

Feature Selection for Classification using Principal Component Analysis and Information Gain

(2021) Erick Odhiambo Omuya et al.   EXPERT SYSTEMS WITH APPLICATIONS

Ghostnet for Hyperspectral Image Classification

(2021) Mercedes E. Paoletti et al.   IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING

One-vs-One classification for deep neural networks

(2020) Pornntiwa Pawara et al.   PATTERN RECOGNITION

Neuro-evolutionary models for imbalanced classification problems

(2020) Israa Al-Badarneh et al.   Journal of King Saud University-Computer and Information Sciences

Integrating Growth and Environmental Parameters to Discriminate Powdery Mildew and Aphid of Winter Wheat Using Bi-Temporal Landsat-8 Imagery

(2019) Huiqin Ma et al.   Remote Sensing

Deep learning classifiers for hyperspectral imaging: A review

(2019) M.E. Paoletti et al.   ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING

Improving imbalanced learning through a heuristic oversampling method based on k-means and SMOTE

(2018) Georgios Douzas et al.   INFORMATION SCIENCES

Machine-Learning Approach to Optimize SMOTE Ratio in Class Imbalance Dataset for Intrusion Detection

(2018) Jae-Hyun Seo et al.   Computational Intelligence and Neuroscience

Comparing Oversampling Techniques to Handle the Class Imbalance Problem: A Customer Churn Prediction Case Study

(2016) Adnan Amin et al.   IEEE Access

Churn prediction using comprehensible support vector machine: An analytical CRM application

(2014) M.A.H. Farquad et al.   APPLIED SOFT COMPUTING

Novel Adaptive Balloon Active Contour Method based on internal force for image segmentation – A systematic evaluation on synthetic and real images

(2014) Pedro Pedrosa Rebouças Filho et al.   EXPERT SYSTEMS WITH APPLICATIONS

A path- and label-cost propagation approach to speedup the training of the optimum-path forest classifier

(2014) A.S. Iwashita et al.   PATTERN RECOGNITION LETTERS

Sow-activity classification from acceleration patterns: A machine learning approach

(2013) Hugo Jair Escalante et al.   COMPUTERS AND ELECTRONICS IN AGRICULTURE