首页期刊导航|Expert systems: The international journal of knowledge engineering
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Expert systems: The international journal of knowledge engineering
Blackwell Publishers Ltd.
Expert systems: The international journal of knowledge engineering

Blackwell Publishers Ltd.

0266-4720

Expert systems: The international journal of knowledge engineering/Journal Expert systems: The international journal of knowledge engineering
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    Nested Named Entity Recognition: A Survey of Latest Research

    Lixia JiYiping DangYunlong DuWenzhao Gao...
    70052.1-70052.20页
    查看更多>>摘要：The research on nested named entity recognition (NER) is conducive to providing richer semantic representations and capturing the nested structure among entities, which is crucial for the execution of downstream tasks. This paper aims to summarise the nested NER methods that have been combined with emerging technologies in recent years. We summarise the nested NER methods that are integrated with emerging technologies from three dimensions: model, framework, and data. Additionally, we explore the research progress of nested NER in two scenarios: cross-lingual modality and multi-modal in different modalities. Furthermore, we discuss the practical applications of NER technology in five fields: biomedicine, justice, finance, media, and e-commerce. Through this review, we can clearly see the development trends of nested NER technology under emerging technologies and different modalities, as well as its broad application prospects in various fields. This provides a reference for future exploration directions in nested NER.
      原文链接:
    • NETL
    • NSTL
    • Wiley

    A Critical Analysis of Generative Adversarial Networks in Anomaly Detection for Time Series

    Marcelo BozzettoMauricio Cagliari TosinTiago Oliveira WeberAlexandre Balbinot...
    70065.1-70065.25页
    查看更多>>摘要：Anomaly detection has applications across different knowledge domains and is intricately linked to numerous problems, such as fault detection for industrial and measurement systems. However, the usual completely unsupervised nature of the problem complicates and restricts the application of various intelligent models. In this context, solutions based on GANs for modelling distributions and arbitrary processes with unsupervised data show potential in anomaly detection. This work addresses a solution based on the TadGAN architecture in the unsupervised detection of anomalies in time series. Initially, a brief review of the state of the art on essential concepts about anomalies in time series is provided, as well as the main works involving GANs in this respective area. Subsequently, the TadGAN architecture is assessed utilising the proposed methodology, wherein its principles and primary limitations are discussed, such as the absence of standardisation in performance evaluation metrics. As an innovation, we assess TadGAN using experimental data and propose new metrics to quantify the anomalous state from both the model and the data. The obtained results confirm the significant potential of GANs in detecting anomalies in time series.
      原文链接:
    • NETL
    • NSTL
    • Wiley

    Explainability of Highly Associated Fuzzy Churn Patterns in Binary Classification

    D. Y. C. WangLars Arne JordangerJerry Chun-Wei Lin
    70066.1-70066.13页
    查看更多>>摘要：Customer churn, particularly in the telecommunications sector, influences both costs and profits. As the explainability of models becomes increasingly important, this study emphasises not only the explainability of customer churn through machine learning models, but also the importance of identifying multivariate patterns and setting soft bounds for intuitive interpretation. The main objective is to use a machine learning model and fuzzy-set theory with top-k HUIM to identify highly associated patterns of customer churn with intuitive identification, referred to as Highly Associated Fuzzy Churn Patterns (HAFCP). Moreover, this method aids in uncovering association rules among multiple features across low, medium, and high distributions. Such discoveries are instrumental in enhancing the explainability of findings. Experiments show that when the top-5 HAFCPs are included in five datasets, a mixture of performance results is observed, with some showing notable improvements. It becomes clear that high importance features enhance explanatory power through their distribution and patterns associated with other features. As a result, the study introduces an innovative approach that improves the explainability and effectiveness of customer churn prediction models.
      原文链接:
    • NETL
    • NSTL
    • Wiley

    Uncertainty-Guided Diffusion Model for High-Fidelity ECG Synthesis and Classification

    Qi ZhangHongyan Li
    70070.1-70070.13页
    查看更多>>摘要：Electrocardiogram (ECG) synthesis plays a crucial role in medical research, education and device development. However, achieving high-fidelity ECG signal synthesis remains challenging, particularly in accurately reproducing specific waveform patterns at the sample level. In this paper, we propose an uncertainty-guided diffusion model that integrates uncertainty estimation into the ECG synthesis process. The uncertainty guidance preserves meaningful waveform characteristics. The model combines diffusion models, known for generating high-quality samples from complex distributions, with uncertainty guidance that captures and propagates uncertainty throughout the pipeline. Extensive experiments demonstrate that our approach outperforms existing methods in terms of both distribution-level and sample-level evaluation.
      原文链接:
    • NETL
    • NSTL
    • Wiley

    Leveraging Ethical Narratives to Enhance LLM-AutoML Generated Machine Learning Models

    Jordan NelsonMichalis PavlidisAndrew FishNikolaos Polatidis...
    70072.1-70072.16页
    查看更多>>摘要：The growing popularity of generative AI and large language models (LLMs) has sparked innovation alongside debate, particularly around issues of plagiarism and intellectual property law. However, a less-discussed concern is the quality of code generated by these models, which often contains errors and encourages poor programming practices. This paper proposes a novel solution by integrating LLMs with automated machine learning (AutoML). By leveraging AutoML's strengths in hyperparameter tuning and model selection, we present a framework for generating robust and reliable machine learning (ML) algorithms. Our approach incorporates natural language processing (NLP) and natural language understanding (NLU) techniques to interpret chatbot prompts, enabling more accurate and customisable ML model generation through AutoML. To ensure ethical AI practices, we have also introduced a filtering mechanism to address potential biases and enhance accountability. The proposed methodology not only demonstrates practical implementation but also achieves high predictive accuracy, offering a viable solution to current challenges in LLM-based code generation. In summary, this paper introduces a new application of NLP and NLU to extract features from chatbot prompts, feeding them into an AutoML system to generate ML algorithms. This approach is framed within a rigorous ethical framework, addressing concerns of bias and accountability while enhancing the reliability of code generation.
      原文链接:
    • NETL
    • NSTL
    • Wiley

    On the Significance of Graph Neural Networks With Pretrained Transformers in Content-Based Recommender Systems for Academic Article Classification

    Jiayun LiuManuel Castillo-CaraRaul Garcia-Castro
    70073.1-70073.16页
    查看更多>>摘要：Recommender systems are tools for interacting with large and complex information spaces by providing a personalised view of such spaces, prioritising items that are likely to be of interest to the user. In addition, they serve as a significant tool in academic research, helping authors select the most appropriate journals for their academic articles. This paper presents a comprehensive study of various journal recommender systems, focusing on the synergy of graph neural networks (GNNs) with pretrained transformers for enhanced text classification. Furthermore, we propose a content-based journal recommender system that combines a pretrained Transformer with a Graph Attention Network (GAT) using title, abstract and keywords as input data. The proposed architecture enhances text representation by forming graphs from the Transformers' hidden states and attention matrices, excluding padding tokens. Our findings highlight that this integration improves the accuracy of the journal recommendations and reduces the transformer oversmoothing problem, with RoBERTa outperforming BERT models. Furthermore, excluding padding tokens from graph construction reduces training time by 8%–15%. Furthermore, we offer a publicly available dataset comprising 830,978 articles.
      原文链接:
    • NETL
    • NSTL
    • Wiley

    Towards Clustering of Incomplete Mixed-Attribute Data

    Chuyao ZhangXinxi ChenZexi TanFangqing Gu...
    70074.1-70074.18页
    查看更多>>摘要：Clustering analysis is one of the most important data mining and knowledge discovery tools in real applications. Since the widespread presence of missing values hampers clustering performance, missing values imputation becomes necessary for data pre-processing. However, for the common datasets composed of both numerical and categorical attributes (also known as mixed-attribute datasets), most existing imputation methods suffer from the following three limitations: (1) Only feasible for a certain type of attribute; (2) Encounter difficulties in considering the interdependence between different types of attributes; (3) Short in exploiting the information provided by the incomplete mix-valued objects. As a result, the original data distribution can be ill-restored, misleading the downstream clustering tasks. This paper therefore proposes a clustering-imputation co-learning method for incomplete mixed-attribute datasets to address these issues. This method integrates imputation and clustering into one learning process, emphasising the interrelationships between mixed attributes during the imputation process and exploiting the information of incomplete objectsduring clustering. It turns out that appropriate recovery of the dataset and accurate clustering can be better achieved through a cross-coupling manner. Experiments on various datasets validate the promising efficacy of the proposed method.
      原文链接:
    • NETL
    • NSTL
    • Wiley

    Optimising Performance Curves for Ensemble Models through Pareto Front Analysis of the Decision Space

    Alberto Gutierrez-GallegoOscar GarnicaDaniel ParraJ. Manuel Velasco...
    70075.1-70075.14页
    查看更多>>摘要：Receiver operating characteristic curves are commonly used to evaluate the performance of machine learning ensemble classification models that combine multiple classifiers through a voting procedure. Although these models have many parameters, standard ROC analyses typically vary only the voting threshold, limiting their potential for improvement. In this paper, we propose Performance Curve Mapping, a new method that redefines the ROC curve as the Pareto front of a multi-objective optimisation problem. The method maps the multidimensional space of all ensemble parameters (Decision space) into a two-dimensional Objective space defined by classification performance metrics. We employ an algorithm based on NSGA-Ⅱ to explore the Decision space and validate the proposal on two different classification problems: (1) predicting car insurance claims in a highly imbalanced dataset (Insurance dataset), and (2) predicting obesity risk in a balanced clinical dataset (GenObIA dataset). We compare our method with alternative ensemble optimisation approaches, using visual assessment, the area under the curve and the Youden index as performance measures. In the Insurance dataset, Performance Curve Mapping achieves an average improvement of 46.4% in AUC-ROC and 26.1% in the Youden index. In the GenObIA dataset, it achieves an average improvement of 29.7% in AUC-ROC and 11.9% in the Youden index. All improvements are calculated relative to the maximum achievable improvement.
      原文链接:
    • NETL
    • NSTL
    • Wiley

    Single and Ensemble Based Filters in Environmental Data

    Yousra CherifAli Idri
    70076.1-70076.56页
    查看更多>>摘要：Researchers rely on species distribution models (SDMs) to establish a correlation between species occurrence records and environmental data. These models offer insights into the ecological and evolutionary aspects of the subject. Feature selection (FS) aims to choose useful interlinked features or remove unnecessary and redundant ones and make the induced model easier to understand. Although feature selection plays a crucial role in SDMs, only a limited number of studies in the literature have addressed it with several key shortcomings such as lack of the use of multivariate techniques, lack of comparison between the univariate and the multivariate filters, and absence of a comparison between the ensemble univariate and multivariate filters. Therefore, this study presents a rigorous empirical evaluation consisting of assessing and comparing six filter-based univariate feature selection methods using two thresholds with two multivariate techniques, as well as four classifiers: Extreme Gradient boosting (XGB), Random Forest (RF), Decision Tree (DT), and Light gradient-boosting machine (LGBM). Furthermore, the current study proposes a novel approach for ensemble construction consisting of evaluating the applications of ensemble learning using 40% of features ranked by means of Borda Count and Reciprocal Rank (univariate filter ensembles) as well as the fusion-based and the intersection-based ensembles (multivariate filter ensembles). Moreover, we evaluated and compared the performances of univariate and multivariate techniques with their ensembles. Similarly, we evaluated and compared the performances of the best ensemble techniques across datasets. The empirical evaluations involve several techniques, such as the 5-fold cross-validation method, the Scott Knott (SK) test, and Borda Count. In addition, we used three performance metrics (accuracy, Kappa, and F1-score). Experiments showed that Consistency-based subset selection in conjunction with RF outperformed all other univariate and multivariate FS techniques with an accuracy value of 91.63% across all datasets. However, Fisher score trained with RF was the best choice when considering the number of features. Moreover, the univariate or multivariate based ensembles, in general, outperformed their singles. In addition, when comparing the univariate and multivariate ensembles, the fusion-based ensemble outperformed all other ensembles achieving an accuracy of 91.77% when using RF across datasets. Nevertheless, in terms of performance and number of features, the ensemble constructed using Reciprocal Rank performed better than all other FS techniques regardless of the classifier used. It achieved an accuracy of 91.61% across datasets when using RF.
      原文链接:
    • NETL
    • NSTL
    • Wiley

    Linearformer: Tri-Net Multi-Layer DVF Medical Image Registration

    Muhammad AnwarZhiyue YanWenming Cao
    70077.1-70077.13页
    查看更多>>摘要：In medical imaging, accurate registration is crucial for reliable analysis. While transformer models demonstrate potential, their application to large datasets like OASIS is constrained by substantial memory requirements, quadratic complexity and the challenge of managing complex deformations. To overcome these challenges, Linearformer is introduced, an efficient transformer-based model with Linear-ProbSparse self-attention for optimised time and memory, along with TNM DVF, a Pyramid-based framework for unsupervised non-rigid registration. Evaluated on OASIS and LPBA40 brain MRI datasets, the model outperforms state-of- the- art methods in Dice score and Jacobian metrics, surpassing TransMatch by 0.6% and 1.9% on the two datasets while maintaining a comparable voxel folding percentage.
      原文链接:
    • NETL
    • NSTL
    • Wiley