13 results on '"Gu, Xiaowei"'
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2. Self-adaptive fuzzy learning ensemble systems with dimensionality compression from data streams.
- Author
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Gu, Xiaowei
- Subjects
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DATA compression , *INSTRUCTIONAL systems , *ONLINE education , *FUZZY systems , *GROUP decision making - Abstract
Ensemble learning is a widely used methodology to build powerful predictors from multiple individual weaker ones. However, the vast majority of ensemble learning models are designed for offline application scenarios, the use of evolving fuzzy systems in ensemble learning for online learning from data streams has not been sufficiently explored, yet. In this paper, a novel self-adaptive fuzzy learning ensemble system is introduced for data stream prediction. The proposed ensemble system employs the very sparse random projection technique to compress the consequent parts of the learned fuzzy rules by individual base models to a more compressed form, thereby reducing redundant information and improving computational efficiency. To improve the overall prediction performance, a dynamical base model pruning scheme is introduced to the proposed ensemble system together with a novel inferencing scheme, such that less accurate base models will be removed from the ensemble structure at each learning cycle automatically and only these more accurate ones will be involved in joint decision-making. Numerical examples based on a wide range of benchmark datasets demonstrate the stronger prediction performance of the proposed ensemble system over the state-of-the-art alternatives. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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3. Self-Organizing Fuzzy Belief Inference System for Classification.
- Author
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Gu, Xiaowei, Angelov, Plamen P., and Shen, Qiang
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FUZZY logic ,KNOWLEDGE base ,FUZZY systems ,EDUCATIONAL outcomes ,CLASSIFICATION ,CLASSIFICATION algorithms - Abstract
Evolving fuzzy systems (EFSs) are widely known as a powerful tool for streaming data prediction. In this article, a novel zero-order EFS with a unique belief structure is proposed for data stream classification. Thanks to this new belief structure, the proposed model can handle the interclass overlaps in a natural way and better capture the underlying multimodel structure of data streams in the form of prototypes. Utilizing data-driven soft thresholds, the proposed model self-organizes a set of prototype-based if–then fuzzy belief rules from data streams for classification, and its learning outcomes are practically meaningful. With no requirement of prior knowledge in the problem domain, the proposed model is capable of self-determining the appropriate level of granularity for rule-based construction, while enabling users to specify their preferences on the degree of fineness of its knowledge base. Numerical examples demonstrate the superior performance of the proposed model on a wide range of stationary and nonstationary classification benchmark problems. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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4. Multiclass Fuzzily Weighted Adaptive-Boosting-Based Self-Organizing Fuzzy Inference Ensemble Systems for Classification.
- Author
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Gu, Xiaowei and Angelov, Plamen P.
- Subjects
FUZZY logic ,FUZZY systems ,SELF-organizing systems ,BOOSTING algorithms ,BENCHMARK problems (Computer science) ,PREDICTION models - Abstract
Adaptive boosting (AdaBoost) is a widely used technique to construct a stronger ensemble classifier by combining a set of weaker ones. Zero-order fuzzy inference systems (FISs) are very powerful prototype-based predictive models for classification, offering both great prediction precision and high user interpretability. However, the use of zero-order FISs as base classifiers in AdaBoost has not been explored yet. To bridge the gap, in this article, a novel multiclass fuzzily weighted AdaBoost (FWAdaBoost)-based ensemble system with a self-organizing fuzzy inference system (SOFIS) as the ensemble component is proposed. To better incorporate the SOFIS, FWAdaBoost utilizes the confidence scores produced by the SOFIS in both sample weight updating and ensemble output generation, resulting in more accurate classification boundaries and greater prediction precision. Numerical examples on a wide range of benchmark classification problems demonstrate the efficacy of the proposed approach. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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- View/download PDF
5. A soft prototype-based autonomous fuzzy inference system for network intrusion detection.
- Author
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Gu, Xiaowei, Howells, Gareth, and Yuan, Haiyue
- Subjects
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FUZZY logic , *FUZZY systems , *COMPUTER network traffic , *DATA structures , *CYBERBULLYING , *ONLINE education , *LEARNING ability - Abstract
Nowadays, cyber-attacks have become a common and persistent issue affecting various human activities in modern societies. Due to the continuously evolving landscape of cyber-attacks and the growing concerns around "black box" models, there has been a strong demand for novel explainable and interpretable intrusion detection systems with online learning abilities. In this paper, a novel soft prototype-based autonomous fuzzy inference system (SPAFIS) is proposed for network intrusion detection. SPAFIS learns from network traffic data streams online on a chunk-by-chunk basis and autonomously identifies a set of meaningful, human-interpretable soft prototypes to build an IF-THEN fuzzy rule base for classification. Thanks to the utilization of soft prototypes, SPAFIS can precisely capture the underlying data structure and local patterns, and perform internal reasoning and decision-making in a human-interpretable manner based on the ensemble properties and mutual distances of data. To maintain a healthy and compact knowledge base, a pruning scheme is further introduced to SPAFIS, allowing itself to periodically examine the learned solution and remove redundant soft prototypes from its knowledge base. Numerical examples on public network intrusion detection datasets demonstrated the efficacy of the proposed SPAFIS in both offline and online application scenarios, outperforming the state-of-the-art alternatives. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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6. Particle Swarm Optimized Autonomous Learning Fuzzy System.
- Author
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Gu, Xiaowei, Shen, Qiang, and Angelov, Plamen P.
- Abstract
The antecedent and consequent parts of a first-order evolving intelligent system (EIS) determine the validity of the learning results and overall system performance. Nonetheless, the state-of-the-art techniques mostly stress on the novelty from the system identification point of view but pay less attention to the optimality of the learned parameters. Using the recently introduced autonomous learning multiple model (ALMMo) system as the implementation basis, this article introduces a particle swarm-based approach for the EIS optimization. The proposed approach is able to simultaneously optimize the antecedent and consequent parameters of ALMMo and effectively enhance the system performance by iteratively searching for optimal solutions in the problem spaces. In addition, the proposed optimization approach does not adversely influence the “one pass” learning ability of ALMMo. Once the optimization process is complete, ALMMo can continue to learn from new data to incorporate unseen data patterns recursively without full retraining. The experimental studies with a number of real-world benchmark problems validate the proposed concept and general principles. It is also verified that the proposed optimization approach can be applied to other types of EISs with similar operating mechanisms. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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7. An explainable semi-supervised self-organizing fuzzy inference system for streaming data classification.
- Author
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Gu, Xiaowei
- Subjects
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FUZZY logic , *FUZZY systems , *CLASSIFICATION , *ELECTRONIC data processing , *MODEL-based reasoning , *PREDICTION models - Abstract
• A novel semi-supervised self-organizing fuzzy inference system (S3OFIS+) is introduced for data stream classification. • A novel parameter-free pseudo-labelling strategy is proposed for S3OFIS+ to effectively self-train from unlabelled data. • The proposed S3OFIS+ can self-learn from unlabelled data streams chunk-by-chunk in a single-pass manner with minimum human input. • The proposed S3OFIS+ can provide users high interpretability and explainability thanks to its prototype-based nature. As a powerful tool for data streams processing, the vast majority of existing evolving intelligent systems (EISs) learn prediction models from data in a supervised manner. However, high-quality labelled data can be difficult to obtain in many real-world classification applications concerning data streams, though unlabelled data is plentiful. To overcome the labelling bottleneck and construct a stronger classification model, a novel semi-supervised EIS is proposed in this paper. After being primed with a small amount of labelled data, the proposed method is capable of continuously self-developing its system structure and self-updating the meta-parameters from unlabelled data streams chunk-by-chunk in a non-iterative, exploratory manner by exploiting a novel pseudo-labelling strategy. Thanks to its transparent prototype-based structure and human-understandable reasoning process, the proposed method can provide users high explainability and interpretability while achieving great classification precision. Experimental investigation demonstrates the superior performance of the proposed method. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
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8. A self-adaptive fuzzy learning system for streaming data prediction.
- Author
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Gu, Xiaowei and Shen, Qiang
- Subjects
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FUZZY systems , *FUZZY logic , *INSTRUCTIONAL systems , *FUZZY sets , *FUZZY numbers , *GROUP decision making - Abstract
In this paper, a novel self-adaptive fuzzy learning (SAFL) system is proposed for streaming data prediction. SAFL self-learns from data streams a predictive model composed of a set of prototype-based fuzzy rules, with each of which representing a certain local data distribution, and continuously self-evolves to follow the changing data patterns in non-stationary environments. Unlike conventional evolving fuzzy systems, both the fuzzy inference and consequent parameter learning schemes utilised by SAFL are simplified so that only a small number of selected fuzzy rules within the rule base are involved in system output generation and parameter updating during a learning cycle. Such simplification not only significantly reduces the system's computational complexity but also increases its prediction precision. In addition, both theoretical and empirical investigations guarantee the stability of the resulting SAFL. Comparative experimental studies on a wide variety of benchmark and real-world problems demonstrate that SAFL is able to learn from streaming data in a highly efficient manner and to make predictions with a great accuracy, revealing the effectiveness and validity of the proposed approach. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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9. Self-boosting first-order autonomous learning neuro-fuzzy systems.
- Author
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Gu, Xiaowei and Angelov, Plamen
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INSTRUCTIONAL systems ,MATHEMATICAL analysis ,SOCIAL problems ,FUZZY systems - Abstract
Abstract In this paper, a detailed mathematical analysis of the optimality of the premise and consequent parts of the recently introduced first-order Autonomous Learning Multi-Model (ALMMo) neuro-fuzzy system is conducted. A novel self-boosting algorithm for structure- and parameter-optimization is, then, introduced to the ALMMo, which results in the self-boosting ALMMo (SBALMMo) neuro-fuzzy system. By minimizing the objective functions with the previously collected data, the SBALMMo is able to optimize its system structure and parameters in few iterations. Numerical examples based benchmark datasets and real-world problems demonstrate the effectiveness and validity of the SBALMMo, and show the strong potential of the proposed approach for real applications. Highlights • A systematic analysis of the optimality of the ALMMo neuro-fuzzy system is conducted. • A novel self-boosting algorithm for structure and parameter-optimization is proposed. • The self-boosting ALMMo (SBALMMo) neuro-fuzzy system is introduced. • The effectiveness and validity of the SBALMMo are demonstrated on real world problems. [ABSTRACT FROM AUTHOR]
- Published
- 2019
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10. Foreign currency exchange rate prediction using neuro-fuzzy systems.
- Author
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Yong, Yoke Leng, Lee, Yunli, Ngo, David Chek Ling, Gu, Xiaowei, Shafipour, Elnaz, and Angelov, Plamen P
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GAUSSIAN mixture models ,FUZZY systems ,FOREIGN exchange rates ,ADAPTIVE filters ,ECONOMETRICS - Abstract
Abstract The complex nature of the foreign exchange (FOREX) market along with the increased interest towards the currency exchange market has prompted extensive research from various academic disciplines. With the inclusion of more in-depth analysis and forecasting methods, traders will be able to make an informed decision when trading. Therefore, an approach incorporating the use of historical data along with computational intelligence for analysis and forecasting is proposed in this paper. Firstly, the Gaussian Mixture Model method is applied for data partitioning on historical observations. While the antecedent part of the neuro-fuzzy system of AnYa type is initialised by the partitioning result, the consequent part is trained using the fuzzily weighted RLS algorithm based on the same data. Numerical examples based on the real currency exchange data demonstrated that the proposed approach trained with historical data produce promising results when used to forecast the future foreign exchange rates over a long-term period. Although implemented in an offline environment, it could potentially be utilised in real-time application in the future. [ABSTRACT FROM AUTHOR]
- Published
- 2018
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11. Stability of Evolving Fuzzy Systems Based on Data Clouds.
- Author
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Rong, Hai-Jun, Angelov, Plamen P., Gu, Xiaowei, and Bai, Jian-Ming
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FUZZY systems ,CLOUD storage ,LYAPUNOV stability ,MEMBERSHIP functions (Fuzzy logic) ,MACHINE learning - Abstract
Evolving fuzzy systems (EFSs) are now well developed and widely used, thanks to their ability to self-adapt both their structures and parameters online. Since the concept was first introduced two decades ago, many different types of EFSs have been successfully implemented. However, there are only very few works considering the stability of the EFSs, and these studies were limited to certain types of membership functions with specifically predefined parameters, which largely increases the complexity of the learning process. At the same time, stability analysis is of paramount importance for control applications and provides the theoretical guarantees for the convergence of the learning algorithms. In this paper, we introduce the stability proof of a class of EFSs based on data clouds, which are grounded at the AnYa type fuzzy systems and the recently introduced empirical data analytics (EDA) methodological framework. By employing data clouds, the class of EFSs of AnYa type considered in this paper avoids the traditional way of defining membership functions for each input variable in an explicit manner and its learning process is entirely data driven. The stability of the considered EFS of AnYa type is proven through the Lyapunov theory, and the proof of stability shows that the average identification error converges to a small neighborhood of zero. Although, the stability proof presented in this paper is specially elaborated for the considered EFS, it is also applicable to general EFSs. The proposed method is illustrated with Box–Jenkins gas furnace problem, one nonlinear system identification problem, Mackey–Glass time series prediction problem, eight real-world benchmark regression problems as well as a high-frequency trading prediction problem. Compared with other EFSs, the numerical examples show that the considered EFS in this paper provides guaranteed stability as well as a better approximation accuracy. [ABSTRACT FROM AUTHOR]
- Published
- 2018
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12. A dual-model semi-supervised self-organizing fuzzy inference system for data stream classification.
- Author
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Gu, Xiaowei
- Subjects
FUZZY logic ,FUZZY systems ,SUPERVISED learning ,BENCHMARK problems (Computer science) ,KNOWLEDGE base ,LEARNING strategies - Abstract
Semi-supervised learning from data streams is widely considered as a highly challenging task to be further researched. In this paper, a novel dual-model self-organizing fuzzy inference system composed of two recently introduced evolving fuzzy systems (EFSs) is proposed for semi-supervised learning from data streams in infinite delay environments. After being primed with a small amount of labelled data during the warm-up period, the proposed model is able to continuously self-learn and self-expand its knowledge base from unlabelled data on a chunk-by-chunk basis with minimal human expert involvement. Thanks to its dual-model structure, the proposed model combines the merits of the two EFS models such that it can continuously identify new prototypes from new pseudo-labelled data to self-improve its knowledge base whilst keeping the impact of pseudo-labelled errors on its decision-making minimized. Numerical examples based on various benchmark problems demonstrate the efficacy of the proposed method, showing its strong potential in real-world applications by offering higher classification accuracy over the state-of-the-art competitors whilst retaining high computational efficiency. • A dual-model semi-supervised system is proposed for data stream classification in infinite label delay scenarios. • A unique semi-supervised learning strategy is designed for its dual-model structure. • The system continuously self-improves its knowledge base from unlabelled streaming data via pseudo-labelling. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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13. Self-organizing fuzzy inference ensemble system for big streaming data classification.
- Author
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Gu, Xiaowei, Angelov, Plamen, and Zhao, Zhijin
- Subjects
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SELF-organizing systems , *PARALLEL programming , *FUZZY systems , *CLASSIFICATION , *PREDICTION models - Abstract
An evolving intelligent system (EIS) is able to self-update its system structure and meta-parameters from streaming data. However, since the majority of EISs are implemented on a single-model architecture, their performances on large-scale, complex data streams are often limited. To address this deficiency, a novel self-organizing fuzzy inference ensemble framework is proposed in this paper. As the base learner of the proposed ensemble system, the self-organizing fuzzy inference system is capable of self-learning a highly transparent predictive model from streaming data on a chunk-by-chunk basis through a human-interpretable process. Very importantly, the base learner can continuously self-adjust its decision boundaries based on the inter-class and intra-class distances between prototypes identified from successive data chunks for higher classification precision. Thanks to its parallel distributed computing architecture, the proposed ensemble framework can achieve great classification precision while maintain high computational efficiency on large-scale problems. Numerical examples based on popular benchmark big data problems demonstrate the superior performance of the proposed approach over the state-of-the-art alternatives in terms of both classification accuracy and computational efficiency. • A novel fuzzy ensemble framework proposed for big streaming data classification. • A novel fuzzy inference system to learn from streaming data chunk-by-chunk. • The base learner to self-calibrate precise decision boundaries from streaming data. • A novel ensemble architecture to build diverse committees with high efficiency. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
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