Journal: Knowledge-Based Systems

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Elsevier

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0950-7051

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2021 - 20258
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Publications1 - 8 of 8
  • Extrapolation-enhanced model for travel decision making: An ensemble machine learning approach considering behavioral theory
    Item type: Journal Article
    Gao, Kun; Yang, Ying; Zhang, Tianshu; et al. (2021)
    Knowledge-Based Systems
    Modeling individuals’ travel decision making in terms of choosing transport modes, route and departure time for daily activities is an indispensable component for transport system optimization and management. Conventional approaches of modeling travel decision making suffer from presumed model structures and parametric specifications. Emerging machine learning algorithms offer data-driven and non-parametric solutions for modeling travel decision making but encounter extrapolation issues (i.e., disability to predict scenarios beyond training samples) due to neglecting behavioral mechanisms in the framework. This study proposes an extrapolation-enhanced approach for modeling travel decision making, leveraging the complementary merits of ensemble machine learning algorithms (Random Forest in our study) and knowledge-based decision-making theory to enhance both predictive accuracy and model extrapolation. The proposed approach is examined using three datasets about travel decision making, including one estimation dataset (for cross-validation) and two test datasets (for model extrapolation tests). Especially, we use two test datasets containing extrapolated choice scenarios with features that exceed the ranges of training samples, to examine the predictive ability of proposed models in extrapolated choice scenarios, which have hardly been investigated by relevant literature. The results show that both proposed models and the direct application of Random Forest (RF) can give quite good predictive accuracy (around 80%) in the estimation dataset. However, RF has a deficient predictive ability in two test datasets with extrapolated choice scenarios. In contrast, the proposed models provide substantially superior predictive performances in the two test datasets, indicating much stronger extrapolation capacity. The model based on the proposed framework could improve the precision score by 274.93% than the direct application of RF in the first test dataset and by 21.9% in the second test dataset. The results indicate the merits of the proposed approach in terms of prediction power and extrapolation ability as compared to existing methods. © 2021 Elsevier
  • Quadratic Optimization based Clique Expansion for overlapping community detection
    Item type: Journal Article
    Yang, Yanhao; Shi, Pan; Wang, Yuyi; et al. (2022)
    Knowledge-Based Systems
    Community detection is a crucial task in the field of network analysis. A community is a collection of tightly connected nodes only have sporadic external connections. In many real-world networks, communities naturally overlap, and prior knowledge about them is usually unavailable, such as the number of ground-truth communities in the network. In this work, we present the QOCE (Quadratic Optimization based Clique Expansion), an overlapping community detection method that does not require any prior knowledge. QOCE follows the popular seed set expansion strategy and regards each high-quality maximal clique as the initial seed set. For seed set expansion, QOCE uses a fast short random walk to sample a subgraph from a clique seed set, then adopts a quadratic optimization to approximate the Cheeger cut on the sampled subgraph. Finally, a local minimum of conductance determines the boundary of the community. We extensively evaluate our method by comparing it with four state-of-the-art baseline algorithms on synthetic and real-world networks in various domains and scales. Empirical results demonstrate the competitive performance of our method in terms of detection accuracy and efficiency.
  • XAI for myo-controlled prosthesis: Explaining EMG data for hand gesture classification
    Item type: Journal Article
    Gozzi, Noemi; Malandri, Lorenzo; Mercorio, Fabio; et al. (2022)
    Knowledge-Based Systems
    Machine Learning has recently found a fertile ground in EMG signal decoding for prosthesis control. However, its understanding and acceptance are strongly limited by the notion of AI models as black-boxes. In critical fields, such as medicine and neuroscience, understanding the neurophysiological phenomena underlying models’ outcomes is as relevant as the classification performances. In this work, we adapt state-of-the-art XAI algorithms to EMG hand gesture classification to understand the outcome of machine learning models with respect to physiological processes, evaluating the contribution of each input feature to the prediction and showing that AI models recognize the hand gestures by mapping and fusing efficiently high amplitude activity of synergic muscles. This allows us to (i) drastically reduce the number of required electrodes without a significant loss in classification performances, ensuring the suitability of the system for a larger population of amputees and simplifying the realization of near real-time applications and (ii) perform an efficient selection of features based on their classification relevance, apprehended by the XAI algorithms. This feature selection leads to classification improvements in term of robustness and computational time, outperforming correlation based methods. Finally, (iii) comparing the physiological explanations produced by the XAI algorithms with the experimental setting highlights inconsistencies in the electrodes positioning over different rounds or users, then improving the overall quality of the process.
  • InfraFFN: A Feature Fusion Network leveraging dual-path convolution and self-attention for infrared image super-resolution
    Item type: Journal Article
    Qin, Feifei; Shen, Zhengwei; Ge, Ruiquan; et al. (2025)
    Knowledge-Based Systems
    Image super-resolution (SR) is a classic visual problem that aims to generate high-quality, high-resolution images from low-resolution inputs. However, most deep learning methods are designed for visible images and often overlook infrared images, which play a crucial role in numerous research fields such as aerospace and remote sensing. Due to hardware limitations, infrared images possess a lower resolution and exhibit characteristics distinct from visible images, including low contrast and indistinct gradients. These unique patterns are challenging to extract and represent. To address this issue, we propose a Infrared Feature Fusion Network (InfraFFN) for infrared image SR in this paper. Specifically, we design a Residual Feature Fusion Block (RFFB) for deep feature extraction. Each Feature Fusion Block (FFB) within RFFB effectively combines the advantages of convolution and self-attention, and utilizes bi-directional information interactions across branches to better model in both channel and spatial dimensions. Furthermore, considering the low contrast in infrared images, we designed a dual-path convolution structure to extract features under different sizes of receptive fields and fuse features at various scales. Extensive experiments demonstrate that our InfraFFN achieves superior visual improvement on multiple infrared image datasets compared to state-of-the-art methods. The source codes are available at https://github.com/szw811/InfraFFN.
  • Towards cross-silo federated learning for corporate organizations
    Item type: Journal Article
    Kalloori, Saikishore; Srivastava, Abhishek (2024)
    Knowledge-Based Systems
    Digital media companies rely on machine learning models to target their content toward their audience's interests. Machine learning models usually rely on the amount and quality of training data. While today, data is abundant, it is typically stored in data silos and cannot be shared between companies or publishers due to data protection and user privacy. Federated Learning (FL) is a distributed machine learning approach that is rapidly gaining popularity and enables collaboratively training machine learning models on a large corpus of decentralized data. Prior research on FL mainly focuses on an FL setup containing millions of clients. For example, a client may be a single user's mobile device with data. However, we note that, in many scenarios, corporate organizations such as news media companies that have available data from multiple sets of users could also benefit from FL. In this work, we aim to focus on building FL models where multiple corporate organizations like news media companies or banks participate in the training process of FL to collaboratively train federated models. We used federated learning to train models for a set of corporate stakeholders and applied FL for two tasks: a classification task and a ranking task. For the classification task, we designed a tree-based federated random forest algorithm and a neural network-based federated algorithm. For the ranking task, we designed a federated neural ranking model for news article recommendations. Our experimental results demonstrate that corporate companies by participating in FL can achieve benefits in improving the model performance in terms of accuracy for classification tasks and in terms of ranking for recommendation tasks. Furthermore, we designed and developed a simple framework for a small number of stakeholders to train federated models.
  • Unsupervised transfer learning for anomaly detection: Application to complementary operating condition transfer
    Item type: Journal Article
    Michau, Gabriel; Fink, Olga (2021)
    Knowledge-Based Systems
    In industrial applications, anomaly detectors are trained to raise alarms when measured samples deviate from the training data distribution. The samples used to train the model should, therefore, be sufficient in quantity and representative of all healthy operating conditions. However, for systems subject to changing operating conditions, acquiring such comprehensive datasets requires a long collection period. To train more robust anomaly detectors, we propose a new framework to perform unsupervised transfer learning (UTL) for one-class classification problems. It differs, thereby, from other applications of UTL in the literature which usually aim at finding a common structure between the datasets to perform either clustering or dimensionality reduction. The task of transferring and combining complementary training data in a completely unsupervised way has not been studied yet. The proposed methodology detects anomalies in operating conditions only experienced by other units in a fleet. We propose the use of adversarial deep learning to ensure the alignment of the different units’ distributions and introduce a new loss, inspired by a dimensionality reduction tool, to enforce the conservation of the inherent variability of each dataset. We use a state-of-the-art once-class approach to detect the anomalies. We demonstrate the benefit of the proposed framework using three open source datasets.
  • Data-driven interpretation of dimensions in an embedding language model based on a reference knowledge graph
    Item type: Journal Article
    Mellina-Andreu , Jose L.; Cisterna-García, Alejandro; Botía , Juan A. (2025)
    Knowledge-Based Systems
    As language models are used in more applications, a key problem has become clear: their numerical embeddings are hard to interpret because it is unclear how each part of the vector relates to real-world meanings in specific fields. The prevailing embedding methods are inadequate in their current state, as they are unable to effectively bridge the gap between mathematical representations and human-understandable knowledge structures. The present study proposes a novel framework that explicitly links ontology classes to specific embedding dimensions through a dual-component architecture combining a text encoder that produces the target embedding dimensions with domain knowledge graphs. The Area Under the Interpretability Curve (AUIC) metric is introduced as a means to systematically evaluate model-alignment with ontological concepts. The analysis reveals that targeted dimensional mapping enables direct interpretation of individual vector components through ontological terms. The practical applications of this framework are illustrated through case studies in biomedical contexts, demonstrating enhanced model transparency without compromising performance. This approach establishes a measurable pathway for reconciling statistical language representations with structured domain knowledge, particularly benefiting fields requiring precise concept alignment like biomedicine. The implementation is publicly available at: https://github.com/Mellandd/DEIBO.
  • Active Learning for Segmentation Based on Bayesian Sample Queries
    Item type: Journal Article
    Ozdemir, Firat; Peng, Zixuan; Fuernstahl, Philipp; et al. (2021)
    Knowledge-Based Systems
    Segmentation of anatomical structures is a fundamental image analysis task for many applications in the medical field. Deep learning methods have been shown to perform well, but for this purpose large numbers of manual annotations are needed in the first place, which necessitate prohibitive levels of resources that are often unavailable. In an active learning framework of selecting informed samples for manual labeling, expert clinician time for manual annotation can be optimally utilized, enabling the establishment of large labeled datasets for machine learning. In this paper, we propose a novel method that combines representativeness with uncertainty in order to estimate ideal samples to be annotated, iteratively from a given dataset. Our novel representativeness metric is based on Bayesian sampling, by using information-maximizing autoencoders. We conduct experiments on a shoulder magnetic resonance imaging (MRI) dataset for the segmentation of four musculoskeletal tissue classes. Quantitative results show that the annotation of representative samples selected by our proposed querying method yields an improved segmentation performance at each active learning iteration, compared to a baseline method that also employs uncertainty and representativeness metrics. For instance, with only 10% of the dataset annotated, our method reaches within 5% of Dice score expected from the upper bound scenario of all the dataset given as annotated (an impractical scenario due to resource constraints), and this gap drops down to a mere 2% when less than a fifth of the dataset samples are annotated. Such active learning approach to selecting samples to annotate enables an optimal use of the expert clinician time, being often the bottleneck in realizing machine learning solutions in medicine.
Publications1 - 8 of 8