Hyperspectral and lidar data applied to the urban land cover machine learning and neural-network-based classification: A review

A Kuras, M Brell, J Rizzi, I Burud - Remote sensing, 2021 - mdpi.com
Rapid technological advances in airborne hyperspectral and lidar systems paved the way
for using machine learning algorithms to map urban environments. Both hyperspectral and …

Class-imbalanced dynamic financial distress prediction based on Adaboost-SVM ensemble combined with SMOTE and time weighting

J Sun, H Li, H Fujita, B Fu, W Ai - Information Fusion, 2020 - Elsevier
This paper focuses on how to effectively construct dynamic financial distress prediction
models based on class-imbalanced data streams. Two class-imbalanced dynamic financial …

Dynamic ensemble selection for imbalanced data streams with concept drift

B Jiao, Y Guo, D Gong, Q Chen - IEEE transactions on neural …, 2022 - ieeexplore.ieee.org
Ensemble learning, as a popular method to tackle concept drift in data stream, forms a
combination of base classifiers according to their global performances. However, concept …

Self-adaptive cost weights-based support vector machine cost-sensitive ensemble for imbalanced data classification

X Tao, Q Li, W Guo, C Ren, C Li, R Liu, J Zou - Information Sciences, 2019 - Elsevier
Imbalanced data classification poses a major challenge in data mining community. Although
standard support vector machine can generally show relatively robust performance in …

Feature selection for imbalanced data based on neighborhood rough sets

H Chen, T Li, X Fan, C Luo - Information sciences, 2019 - Elsevier
Feature selection is a meaningful aspect of data mining that aims to select more relevant
data features and provide more concise and explicit data descriptions. It is beneficial for …

Feature reduction for imbalanced data classification using similarity-based feature clustering with adaptive weighted k-nearest neighbors

L Sun, J Zhang, W Ding, J Xu - Information Sciences, 2022 - Elsevier
Most existing imbalanced data classification models mainly focus on the classification
performance of majority class samples, and many clustering algorithms need to manually …

[HTML][HTML] A comprehensive active learning method for multiclass imbalanced data streams with concept drift

W Liu, H Zhang, Z Ding, Q Liu, C Zhu - Knowledge-Based Systems, 2021 - Elsevier
A challenge to many real-world applications is multiclass imbalance with concept drift. In this
paper, we propose a comprehensive active learning method for multiclass imbalanced …

[PDF][PDF] Improving depression prediction accuracy using fisher score-based feature selection and dynamic ensemble selection approach based on acoustic features of …

N Janardhan, N Kumaresh - Traitement du Signal, 2022 - researchgate.net
Accepted: 13 February 2022 Depression affects over 322 million people, and it is the most
common source of disability worldwide. Literature in speech processing revealed that …

Product backorder prediction using deep neural network on imbalanced data

M Shajalal, P Hajek, MZ Abedin - International Journal of …, 2023 - Taylor & Francis
Taking backorders on products is a common scenario in inventory and supply chain
management systems. The ability to predict the likelihood of backorders can surely minimise …

Clustering-guided particle swarm feature selection algorithm for high-dimensional imbalanced data with missing values

Y Zhang, YH Wang, DW Gong… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Feature selection (FS) in data with class imbalance or missing values has received much
attention from researchers due to their universality in real-world applications. However, for …