Meta-ADD: A meta-learning based pre-trained model for concept drift active detection
Abstract Concept drift is a phenomenon that commonly happened in data streams and need
to be detected, because it means the statistical properties of a target variable, which the …
to be detected, because it means the statistical properties of a target variable, which the …
Improving embedded knowledge graph multi-hop question answering by introducing relational chain reasoning
W **, B Zhao, H Yu, X Tao, R Yin, G Liu - Data Mining and Knowledge …, 2023 - Springer
Abstract Knowledge Graph Question Answering (KGQA) aims to answer user-questions from
a knowledge graph (KG) by identifying the reasoning relations between topic entity and …
a knowledge graph (KG) by identifying the reasoning relations between topic entity and …
Elastic gradient boosting decision tree with adaptive iterations for concept drift adaptation
As an excellent ensemble algorithm, Gradient Boosting Decision Tree (GBDT) has been
tested extensively with static data. However, real-world applications often involve dynamic …
tested extensively with static data. However, real-world applications often involve dynamic …
Topology learning-based fuzzy random neural networks for streaming data regression
As a type of evolving-fuzzy system, the evolving-fuzzy-neuro (EFN) system uses the structure
inspired by neural networks to determine its parameters (fuzzy sets and fuzzy rules), so EFN …
inspired by neural networks to determine its parameters (fuzzy sets and fuzzy rules), so EFN …
Intuitionistic fuzzy weighted least squares twin SVMs
Fuzzy membership is an effective approach used in twin support vector machines (SVMs) to
reduce the effect of noise and outliers in classification problems. Fuzzy twin SVMs …
reduce the effect of noise and outliers in classification problems. Fuzzy twin SVMs …
Learning data streams with changing distributions and temporal dependency
In a data stream, concept drift refers to unpredictable distribution changes over time, which
violates the identical-distribution assumption required by conventional machine learning …
violates the identical-distribution assumption required by conventional machine learning …
Real-time prediction system of train carriage load based on multi-stream fuzzy learning
When a train leaves a platform, knowing the carriage load (the number of passengers in
each carriage) of this train will support train managers to guide passengers at the next …
each carriage) of this train will support train managers to guide passengers at the next …
Memory shapelet learning for early classification of streaming time series
X Wan, L Cen, X Chen, Y **e… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Early classification predicts the class of the incoming sequences before it is completely
observed. How to quickly classify streaming time series without losing interpretability …
observed. How to quickly classify streaming time series without losing interpretability …
Concept drift adaptation by exploiting drift type
Concept drift is a phenomenon where the distribution of data streams changes over time.
When this happens, model predictions become less accurate. Hence, models built in the …
When this happens, model predictions become less accurate. Hence, models built in the …
Hybrid Machine Learning Approach for Evapotranspiration Estimation of Fruit Tree in Agricultural Cyber–Physical Systems
T Wang, X Wang, Y Jiang, Z Sun… - IEEE Transactions …, 2022 - ieeexplore.ieee.org
The flourish of the Internet of Things (IoT) and data-driven techniques provide new ideas for
enhancing agricultural production, where evapotranspiration estimation is a crucial issue in …
enhancing agricultural production, where evapotranspiration estimation is a crucial issue in …