Results 261 to 270 of about 6,303,129 (314)

HELPHED: Hybrid Ensemble Learning PHishing Email Detection

Journal of Network and Computer Applications, 2022
Phishing email attack is a dominant cyber-criminal strategy for decades. Despite its longevity, it has evolved during the COVID-19 pandemic, indicating that adversaries exploit critical situations to lure victims.
Panagiotis Bountakas, C. Xenakis
semanticscholar   +1 more source

Intelligent Fault Diagnosis by Fusing Domain Adversarial Training and Maximum Mean Discrepancy via Ensemble Learning

IEEE Transactions on Industrial Informatics, 2021
Nowadays, the industrial Internet of Things (IIoT) has been successfully utilized in smart manufacturing. The massive amount of data in IIoT promote the development of deep learning-based health monitoring for industrial equipment.
Yibin Li, Yan Song, Lei Jia, Shengyao Gao, Qiqiang Li, M. Qiu   +5 more
semanticscholar   +1 more source

A 100 m gridded population dataset of China's seventh census using ensemble learning and big geospatial data

Earth System Science Data
. China has undergone rapid urbanization and internal migration in the past few years, and its up-to-date gridded population datasets are essential for various applications. Existing datasets for China, however, suffer from either outdatedness or failure
Yuehong Chen, Congcong Xu, Yong Ge, Xiaoxiang Zhang, Ya'nan Zhou   +4 more
semanticscholar   +1 more source

Transfer Learning and Ensemble Learning

2020
In this chapter, we start from transfer learning and introduce the relationship between different learners; we use ensemble learning to combine them together and hope to get a strong learner from a weak learner by changing the training dataset or adjusting parameters of networks. Our ultimate goal is to implement a robust and stable classifier.
openaire   +1 more source

Deep Learning for Industrial KPI Prediction: When Ensemble Learning Meets Semi-Supervised Data

IEEE Transactions on Industrial Informatics, 2021
Soft-sensing techniques are of great significance in industrial processes for monitoring and prediction of key performance indicators. Due to the effectiveness of nonlinear feature extraction and strong expansibility, an autoencoder (AE) and its ...
Qingqiang Sun, Zhiqiang Ge
semanticscholar   +1 more source

An Ensemble Learning-Based Data-Driven Method for Online State-of-Health Estimation of Lithium-Ion Batteries

IEEE Transactions on Transportation Electrification, 2021
The state-of-health (SOH) estimation of lithium-ion batteries (LIBs) is of great importance to the safety of systems. In this article, a novel ensemble learning method is proposed to accurately estimate the SOH of LIBs.
Bin Gou, Yan Xu, Xue Feng
semanticscholar   +1 more source

Ensemble learning

Zhiyuan Liu, Ziyuan Gu, Pan Liu
  +8 more sources

Reduced ensemble size stacking [ensemble learning]

16th IEEE International Conference on Tools with Artificial Intelligence, 2005
We investigate an algorithmic extension to the technique of stacked regression that prunes the size of a homogeneous ensemble set based on a consideration of the accuracy and diversity of the set members. We show that the pruned ensemble set is as accurate on average over the data-sets tested as the nonpruned version, which provides benefits in terms ...
N. Rooney, D. Patterson, C. Nugent
openaire   +1 more source

Spatial Ensemble Learning

2017
This chapter introduces a novel ensemble learning framework called spatial ensemble, which is used to classify heterogeneous spatial data with class ambiguity. Class ambiguity refers to the phenomenon whereby samples with similar features belong to different classes at different locations (e.g., spectral confusion between different thematic classes in ...
Zhe Jiang, Shashi Shekhar
openaire   +1 more source

Ensemble Learning Approaches

2015
As mentioned in Chap. 1, ensemble learning is helpful to improve overall accuracy of classification. This chapter introduces three approaches of ensemble learning namely, parallel learning, sequential learning and hybrid learning. In particular, some popular methods for ensemble learning, such as Bagging and Boosting, are illustrated in detail.
Han Liu, Alexander Gegov, Mihaela Cocea
openaire   +1 more source