Xiaowei Gu

About

Areas of specialism

Machine Learning; Artificial Intelligence; Data Analytics; Signal Processing

My qualifications

2021
Fellow of HEA

Research

Research interests

Research projects

Supervision

Postgraduate research supervision

Publications

Highlights

My Google Scholar Page

My ResearchGate Page

My GitHub Page

Xiaowei Gu, Plamen P. Angelov, Qiang Shen (2024)Semi-Supervised Fuzzily Weighted Adaptive Boosting for Classification, In: IEEE TRANSACTIONS ON FUZZY SYSTEMS(1) IEEE

—Fuzzy systems offer a formal and practically popular methodology for modelling nonlinear problems with inherent uncertainties, entailing strong performance and model interpretability. Particularly, semi-supervised boosting is widely recognised as a powerful approach for creating stronger ensemble classification models in the absence of sufficient labelled data without introducing any modification to the employed base classifiers. However, the potential of fuzzy systems in semi-supervised boosting has not been systematically explored yet. In this study, a novel semi-supervised boosting algorithm devised for zero-order evolving fuzzy systems is proposed. It ensures both the consistence amongst predictions made by individual base classifiers at successive boosting iterations and the respective levels of confidence towards their predictions throughout the process of sample weight updating and ensemble output generation. In so doing, the base classifiers are empowered to gradually focus more on challenging samples that are otherwise hard to generalise, enabling the development of more precise integrated classification boundaries. Numerical evaluations on a range of benchmark problems are carried out, demonstrating the efficacy of the proposed semi-supervised boosting algorithm for constructing ensemble fuzzy classifiers with high accuracy.

Hongxing Cui, Danling Tang, Huizeng Liu, Hongbin Liu, Yi Sui, Yangchen Lai, Xiaowei Gu (2024)Modelling Ocean Cooling Induced by Tropical Cyclone Wind Pump Using Explainable Machine Learning Framework, In: IEEE transactions on geoscience and remote sensing : a publication of the IEEE Geoscience and Remote Sensing Society IEEE

—Tropical cyclones (TCs), with an intensive wind pump impact, induce sea surface temperature cooling (SSTC) on the upper ocean. SSTC is a pronounced indicator to reveal TC evolution and oceanic conditions. However, there are few effective methods for accurately approximating the amplitude of the spatial structure of TC-induced SSTC. This study proposes a novel explainable machine learning framework to model and interpret the amplitude of the spatial structure of SSTC over the northwest Pacific (NWP). In particular, 12 predictors related to TC characteristics and pre-storm ocean states are considered as inputs. A composite analysis technique is used to characterize the amplitude of the spatial structure of SSTC across the TC track. Extreme gradient boosting (XGBoost) is utilized to predict the amplitude of SSTC from the 12 predictors. To better interpret the ocean-atmosphere interaction, a SHapely Additive explanations (SHAP) method is further employed to identify the contributions of predictors in determining the amplitude of the TC-induced SSTC, bringing the attribute-oriented explainability to the proposed method. Results showed that the proposed method could accurately predict the amplitude of the spatial structure of SSTC for different TC intensity groups and outperforms a numerical model. The proposed method also serves as an effective tool for reconstructing composite maps of both interannual and seasonal evolutions of SSTC spatial structure. The study offers insight into applying machine learning to model and interpret the responses of oceanic conditions triggered by extreme weather conditions (e.g., TCs). Index Terms—Tropical cyclone; sea surface temperature cooling; explainable machine learning; wind pump.

Muhammad Yunus Bin Iqbal Basheer, Azliza Mohd Ali, Nurzeatul Hamimah Abdul Hamid, Muhammad Azizi Mohd Ariffin, Rozianawaty Osman, Sharifalillah Nordin, Xiaowei Gu (2023)Autonomous Anomaly Detection for Streaming Data, In: Knowledge-based systems [e-journal]111235

Anomaly detection from data streams is a hotly studied topic in the machine learning domain. It is widely considered a challenging task because the underlying patterns exhibited by the streaming data may dynamically change at any time. In this paper, a new algorithm is proposed to detect anomalies autonomously for streaming data. The proposed algorithm is nonparametric and does not require any threshold to be preset by users. The algorithmic procedure of the proposed algorithm is composed of the following three complementary stages. Firstly, the potentially anomalous samples that represent highly different patterns from others are identified from data streams based on data density. Then, these potentially anomalous samples are clustered online using the evolving autonomous data partitioning algorithm. Finally, true anomalies are identified from these minor clusters with the least amounts of samples associated with them. Numerical examples based on three benchmark datasets demonstrated the potential of the proposed algorithm as a highly effective approach for anomaly detection from data streams. Crown