Outputs (104)

Self-Bidirectional Decoupled Distillation for Time Series Classification (2024)
Journal Article
Xiao, Z., Xing, H., Qu, R., Li, H., Feng, L., Zhao, B., & Yang, J. (2024). Self-Bidirectional Decoupled Distillation for Time Series Classification. IEEE Transactions on Artificial Intelligence, 5(8), 4101-4110. https://doi.org/10.1109/tai.2024.3360180

Over the years, many deep learning algorithms have been developed for time series classification (TSC). A learning model’s performance usually depends on the quality of the semantic information extracted from lower and higher levels within the repres... Read More about Self-Bidirectional Decoupled Distillation for Time Series Classification.

Densely Knowledge-Aware Network for Multivariate Time Series Classification (2024)
Journal Article
Xiao, Z., Xing, H., Qu, R., Feng, L., Luo, S., Dai, P., Zhao, B., & Dai, Y. (2024). Densely Knowledge-Aware Network for Multivariate Time Series Classification. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 54(4), 2192-2204. https://doi.org/10.1109/tsmc.2023.3342640

Multivariate time series classification (MTSC) based on deep learning (DL) has attracted increasingly more research attention. The performance of a DL-based MTSC algorithm is heavily dependent on the quality of the learned representations providing s... Read More about Densely Knowledge-Aware Network for Multivariate Time Series Classification.

Discrete-Time Survival Models with Neural Networks for Age-Period-Cohort Analysis of Credit Risk (2024)
Preprint / Working Paper
Wang, H., Bellotti, A. G., Qu, R., & Bai, R. (2024). Discrete-Time Survival Models with Neural Networks for Age-Period-Cohort Analysis of Credit Risk

Survival models have become popular for credit risk estimation. Most current credit risk survival models use an underlying linear model. This is beneficial in terms of interpretability but is restrictive for real-life applications since it cannot dis... Read More about Discrete-Time Survival Models with Neural Networks for Age-Period-Cohort Analysis of Credit Risk.

CapMatch: Semi-Supervised Contrastive Transformer Capsule With Feature-Based Knowledge Distillation for Human Activity Recognition (2023)
Journal Article
Xiao, Z., Tong, H., Qu, R., Xing, H., Luo, S., Zhu, Z., Song, F., & Feng, L. (2025). CapMatch: Semi-Supervised Contrastive Transformer Capsule With Feature-Based Knowledge Distillation for Human Activity Recognition. IEEE Transactions on Neural Networks and Learning Systems, 36(2), 2690-2704. https://doi.org/10.1109/TNNLS.2023.3344294

This article proposes a semi-supervised contrastive capsule transformer method with feature-based knowledge distillation (KD) that simplifies the existing semisupervised learning (SSL) techniques for wearable human activity recognition (HAR), called... Read More about CapMatch: Semi-Supervised Contrastive Transformer Capsule With Feature-Based Knowledge Distillation for Human Activity Recognition.

Container port truck dispatching optimization using Real2Sim based deep reinforcement learning (2023)
Journal Article
Jin, J., Cui, T., Bai, R., & Qu, R. (2024). Container port truck dispatching optimization using Real2Sim based deep reinforcement learning. European Journal of Operational Research, 315(1), 161-175. https://doi.org/10.1016/J.EJOR.2023.11.038

In marine container terminals, truck dispatching optimization is often considered as the primary focus as it provides crucial synergy between the sea-side operations and yard-side activities and hence can greatly affect the terminal throughput and qu... Read More about Container port truck dispatching optimization using Real2Sim based deep reinforcement learning.

Automated design of local search algorithms: Predicting algorithmic components with LSTM (2023)
Journal Article
Meng, W., & Qu, R. (2024). Automated design of local search algorithms: Predicting algorithmic components with LSTM. Expert Systems with Applications, 237(Part A), Article 121431. https://doi.org/10.1016/j.eswa.2023.121431

With a recently defined AutoGCOP framework, the design of local search algorithms has been defined as the composition of elementary algorithmic components. The effective compositions of the best algorithms thus retain useful knowledge of effective al... Read More about Automated design of local search algorithms: Predicting algorithmic components with LSTM.

Automated design of search algorithms based on reinforcement learning (2023)
Journal Article
Yi, W., & Qu, R. (2023). Automated design of search algorithms based on reinforcement learning. Information Sciences, 649, Article 119639. https://doi.org/10.1016/j.ins.2023.119639

Automated algorithm design has attracted increasing research attention recently in the evolutionary computation community. The main design decisions include selection heuristics and evolution operators in the search algorithms. Most existing studies,... Read More about Automated design of search algorithms based on reinforcement learning.

Deep Contrastive Representation Learning With Self-Distillation (2023)
Journal Article
Xiao, Z., Xing, H., Zhao, B., Qu, R., Luo, S., Dai, P., Li, K., & Zhu, Z. (2024). Deep Contrastive Representation Learning With Self-Distillation. IEEE Transactions on Emerging Topics in Computational Intelligence, 8(1), 3-15. https://doi.org/10.1109/tetci.2023.3304948

Recently, contrastive learning (CL) is a promising way of learning discriminative representations from time series data. In the representation hierarchy, semantic information extracted from lower levels is the basis of that captured from higher level... Read More about Deep Contrastive Representation Learning With Self-Distillation.

Guest Editorial Special Issue on Multiobjective Evolutionary Optimization in Machine Learning (2023)
Journal Article
Aickelin, U., Khorshidi, H. A., Qu, R., & Charkhgard, H. (2023). Guest Editorial Special Issue on Multiobjective Evolutionary Optimization in Machine Learning. IEEE Transactions on Evolutionary Computation, 27(4), 746-748. https://doi.org/10.1109/tevc.2023.3292528

We are very pleased to introduce this special issue on multiobjective evolutionary optimization for machine learning (MOML). Optimization is at the heart of many machine-learning techniques. However, there is still room to exploit optimization in mac... Read More about Guest Editorial Special Issue on Multiobjective Evolutionary Optimization in Machine Learning.

Sequential Rule Mining for Automated Design of Meta-heuristics (2023)
Presentation / Conference Contribution
Meng, W., & Qu, R. (2023, July). Sequential Rule Mining for Automated Design of Meta-heuristics. Presented at GECCO 2023 Companion - Proceedings of the 2023 Genetic and Evolutionary Computation Conference Companion, New York, USA

With a recently defined AutoGCOP framework, the design of local search algorithms can be defined as the composition of the basic elementary algorithmic components. These compositions into the best algorithms thus retain useful knowledge of effective... Read More about Sequential Rule Mining for Automated Design of Meta-heuristics.