Information Fusion最新文献

Pan-Mamba: Effective pan-sharpening with state space model 泛曼巴利用状态空间模型进行有效的平移锐化

IF 14.7 1区计算机科学

Information Fusion Pub Date : 2024-11-08 DOI: 10.1016/j.inffus.2024.102779

Xuanhua He , Ke Cao , Jie Zhang , Keyu Yan , Yingying Wang , Rui Li , Chengjun Xie , Danfeng Hong , Man Zhou

{"title":"Pan-Mamba: Effective pan-sharpening with state space model","authors":"Xuanhua He , Ke Cao , Jie Zhang , Keyu Yan , Yingying Wang , Rui Li , Chengjun Xie , Danfeng Hong , Man Zhou","doi":"10.1016/j.inffus.2024.102779","DOIUrl":"10.1016/j.inffus.2024.102779","url":null,"abstract":"<div><div>Pan-sharpening involves integrating information from low-resolution multi-spectral and high-resolution panchromatic images to generate high-resolution multi-spectral counterparts. While recent advancements in the state space model, particularly the efficient long-range dependency modeling achieved by Mamba, have revolutionized computer vision community, its untapped potential in pan-sharpening motivates our exploration. Our contribution, Pan-Mamba, represents a novel pan-sharpening network that leverages the efficiency of the Mamba model in global information modeling. In Pan-Mamba, we customize two core components: channel swapping Mamba and cross-modal Mamba, strategically designed for efficient cross-modal information exchange and fusion. The former initiates a lightweight cross-modal interaction through the exchange of partial panchromatic and multi-spectral channels, while the latter facilities the information representation capability by exploiting inherent cross-modal relationships. Through extensive experiments across diverse datasets, our proposed approach surpasses state-of-the-art methods, showcasing superior fusion results in pan-sharpening. To the best of our knowledge, this work is the first attempt in exploring the potential of the Mamba model and establishes a new frontier in the pan-sharpening techniques. The source code is available at <span><span>https://github.com/alexhe101/Pan-Mamba</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"115 ","pages":"Article 102779"},"PeriodicalIF":14.7,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

FairDPFL-SCS: Fair Dynamic Personalized Federated Learning with strategic client selection for improved accuracy and fairness FairDPFL-SCS：公平动态个性化联合学习，通过策略性客户选择提高准确性和公平性

IF 14.7 1区计算机科学

Information Fusion Pub Date : 2024-11-06 DOI: 10.1016/j.inffus.2024.102756

Fahad Sabah , Yuwen Chen , Zhen Yang , Abdul Raheem , Muhammad Azam , Nadeem Ahmad , Raheem Sarwar

{"title":"FairDPFL-SCS: Fair Dynamic Personalized Federated Learning with strategic client selection for improved accuracy and fairness","authors":"Fahad Sabah , Yuwen Chen , Zhen Yang , Abdul Raheem , Muhammad Azam , Nadeem Ahmad , Raheem Sarwar","doi":"10.1016/j.inffus.2024.102756","DOIUrl":"10.1016/j.inffus.2024.102756","url":null,"abstract":"<div><div>Personalized federated learning (PFL) addresses the significant challenge of non-independent and identically distributed (non-IID) data across clients in federated learning (FL). Our proposed framework, “FairDPFL-SCS: Fair Dynamic Personalized Federated Learning with Strategic Client Selection”, marks a notable advancement in this domain. By integrating dynamic learning rate adjustments and a strategic client selection mechanism, our approach effectively mitigates the challenges posed by non-IID data while enhancing model personalization, fairness, and efficiency. We evaluated FairDPFL-SCS using standard datasets, including MNIST, FashionMNIST, and SVHN, employing architectures like VGG and CNN. Our model achieved impressive results, attaining 99.04% accuracy on MNIST, 89.19% on FashionMNIST, and 90.9% on SVHN. These results represent a substantial improvement over existing methods, including a highest increase of 16.74% in accuracy on SVHN when compared to the best-performing benchmark methods. In particular, our method also demonstrated lower fairness variance, presenting the importance of fairness in model personalization, a frequently overlooked aspect in FL research. Through extensive experiments, we validate the superior performance of FairDPFL-SCS compared to benchmark PFL approaches, highlighting significant improvements over state-of-the-art methods. This work represents a promising step forward in the field of federated learning, offering a comprehensive solution to the challenges presented by non-IID data while prioritizing fairness and efficiency in model personalization.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"115 ","pages":"Article 102756"},"PeriodicalIF":14.7,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

M-IPISincNet: An explainable multi-source physics-informed neural network based on improved SincNet for rolling bearings fault diagnosis M-IPISincNet：基于改进 SincNet 的可解释多源物理信息神经网络，用于滚动轴承故障诊断

IF 14.7 1区计算机科学

Information Fusion Pub Date : 2024-11-06 DOI: 10.1016/j.inffus.2024.102761

Jingshu Zhong , Yu Zheng , Chengtao Ruan , Liang Chen , Xiangyu Bao , Lyu Lyu

{"title":"M-IPISincNet: An explainable multi-source physics-informed neural network based on improved SincNet for rolling bearings fault diagnosis","authors":"Jingshu Zhong , Yu Zheng , Chengtao Ruan , Liang Chen , Xiangyu Bao , Lyu Lyu","doi":"10.1016/j.inffus.2024.102761","DOIUrl":"10.1016/j.inffus.2024.102761","url":null,"abstract":"<div><div>Timely and accurate diagnosis of bearing faults can effectively reduce the chance of accidents in equipment. However, deep learning methods are mostly completely dependent on data and lack interpretability. It is difficult to deal with the differences between real-time data and training data under changing working conditions and noisy environments. In this study, we proposed M-IPISincNet, an explainability multi-source physics-informed convolutional network based on improved SincNet. Rolling bearing fault diagnosis is realized by extracting fault features from vibration and current signals. Firstly, a physics-informed convolutional layer is designed based on inverse Fourier transform and bandpass filters. Fault features are extracted by multi-scale convolution and multi-layer nonlinear mapping. A DBN network is applied extract unsupervised hidden fusion features in the vibration and current signals. The proposed method is validated under the datasets of Paderborn University (PU) and Case Western Reserve University (CWRU), which proves that the proposed method has explainability, robustness and great accuracy under multiple working conditions and noises.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"115 ","pages":"Article 102761"},"PeriodicalIF":14.7,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An autoencoder-based confederated clustering leveraging a robust model fusion strategy for federated unsupervised learning 基于自动编码器的联合聚类，利用稳健的模型融合策略实现联合无监督学习

IF 14.7 1区计算机科学

Information Fusion Pub Date : 2024-11-06 DOI: 10.1016/j.inffus.2024.102751

Nahid Hasan , Md. Golam Rabiul Alam , Shamim H. Ripon , Phuoc Hung Pham , Mohammad Mehedi Hassan

{"title":"An autoencoder-based confederated clustering leveraging a robust model fusion strategy for federated unsupervised learning","authors":"Nahid Hasan , Md. Golam Rabiul Alam , Shamim H. Ripon , Phuoc Hung Pham , Mohammad Mehedi Hassan","doi":"10.1016/j.inffus.2024.102751","DOIUrl":"10.1016/j.inffus.2024.102751","url":null,"abstract":"<div><div>Concerns related to data privacy, security, and ethical considerations become more prominent as data volumes continue to grow. In contrast to centralized setups, where all data is accessible at a single location, model-based clustering approaches can be successfully employed in federated settings. However, this approach to clustering in federated settings is still relatively unexplored and requires further attention. As federated clustering deals with remote data and requires privacy and security to be maintained, it poses particular challenges as well as possibilities. While model-based clustering offers promise in federated environments, a robust model aggregation method is essential for clustering rather than the generic model aggregation method like Federated Averaging (FedAvg). In this research, we proposed an autoencoder-based clustering method by introducing a novel model aggregation method FednadamN, which is a fusion of Adam and Nadam optimization approaches in a federated learning setting. Therefore, the proposed FednadamN adopted the adaptive learning rates based on the first and second moments of gradients from Adam which offered fast convergence and robustness to noisy data. Furthermore, FednadamN also incorporated the Nesterov-accelerated gradients from Nadam to further enhance the convergence speed and stability. We have studied the performance of the proposed Autoencoder-based clustering methods on benchmark datasets and using the novel FednadamN model aggregation strategy. It shows remarkable performance gain in federated clustering in comparison to the state-of-the-art.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"115 ","pages":"Article 102751"},"PeriodicalIF":14.7,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Credibility-based multi-sensor fusion for non-Gaussian conversion error mitigation 基于可信度的多传感器融合，减少非高斯转换误差

IF 14.7 1区计算机科学

Information Fusion Pub Date : 2024-11-05 DOI: 10.1016/j.inffus.2024.102704

Quanbo Ge , Kai Lin , Zhongyuan Zhao

{"title":"Credibility-based multi-sensor fusion for non-Gaussian conversion error mitigation","authors":"Quanbo Ge , Kai Lin , Zhongyuan Zhao","doi":"10.1016/j.inffus.2024.102704","DOIUrl":"10.1016/j.inffus.2024.102704","url":null,"abstract":"<div><div>In a complex environment, a multi-sensor fusion algorithm can compensate for the limitations of a single sensor’s performance. In a distributed fusion algorithm, sensors need to transmit local estimates to a central coordinate system, and the existence of coordinate transformation uncertainty can undermine the performance of data transmission. Therefore, this paper proposes a multi-sensor distributed fusion method based on trustworthiness. Firstly, considering the presence of non-Gaussian conversion errors, a credibility-based multi-sensor fusion framework is constructed. Secondly, to address the difficulty in estimating conversion errors when measurement errors follow a non-Gaussian distribution, an optimization model is constructed based on actual measurement information to estimate the distribution of non-Gaussian conversion errors. Then, in response to the non-linear and non-Gaussian characteristics of the target optimization function, a particle swarm optimization algorithm based on trustworthiness adaptive weights is proposed to estimate the coordinate transformation errors. Finally, given the inconsistency in local estimates due to missing sensor measurements or significant errors in a non-Gaussian complex environment, a maximum correntropy consensus algorithm is proposed to avoid the trustworthiness calculation being affected by the current measurement errors, thereby improving the accuracy of the global estimation.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"115 ","pages":"Article 102704"},"PeriodicalIF":14.7,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-level information fusion for missing multi-label learning based on stochastic concept clustering 基于随机概念聚类的多标签缺失学习的多层次信息融合

IF 14.7 1区计算机科学

Information Fusion Pub Date : 2024-11-02 DOI: 10.1016/j.inffus.2024.102775

Zhiming Liu , Jinhai Li , Xiao Zhang , Xizhao Wang

{"title":"Multi-level information fusion for missing multi-label learning based on stochastic concept clustering","authors":"Zhiming Liu , Jinhai Li , Xiao Zhang , Xizhao Wang","doi":"10.1016/j.inffus.2024.102775","DOIUrl":"10.1016/j.inffus.2024.102775","url":null,"abstract":"<div><div>Missing multi-label learning is to address the problem of missing labels in multi-label datasets for multi-label classification tasks. Notably, the complex dependencies that typically exist between labels make accurate classification particularly challenging in the presence of missing labels. Some existing missing multi-label classification models often utilize feature selection to effectively recognize the dependencies between labels and features. However, they are ineffective at capturing hierarchical relationships of feature information, probably leading to a decline in prediction performance. To address this problem, this paper proposes a missing multi-label classification model based on multi-level stochastic concept clustering (MML-MSCC) to make dependencies between features and labels recognized more accurately and prediction performance better. In our model, optimal granularity selection is achieved through the global mutual information between features and labels, which makes the study of stochastic granule concept across multiple granularities. Furthermore, we utilize a stochastic concept clustering method to combine similar feature information for the purpose of making the missing label completion more reasonable. Note that stochastic granule concept clustering is performed with cross-granularity, thereby effectively capturing hierarchical relationships among feature information. Finally, to evaluate the performance of our model, we compare the MML-MSCC model with 9 existing missing multi-label classification models on 12 open datasets in terms of six evaluation metrics.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"115 ","pages":"Article 102775"},"PeriodicalIF":14.7,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust Mixed-order Graph Learning for incomplete multi-view clustering 用于不完全多视角聚类的鲁棒混序图学习

IF 14.7 1区计算机科学

Information Fusion Pub Date : 2024-11-02 DOI: 10.1016/j.inffus.2024.102776

Wei Guo , Hangjun Che , Man-Fai Leung , Long Jin , Shiping Wen

{"title":"Robust Mixed-order Graph Learning for incomplete multi-view clustering","authors":"Wei Guo , Hangjun Che , Man-Fai Leung , Long Jin , Shiping Wen","doi":"10.1016/j.inffus.2024.102776","DOIUrl":"10.1016/j.inffus.2024.102776","url":null,"abstract":"<div><div>Incomplete multi-view clustering (IMVC) aims to address the clustering problem of multi-view data with partially missing samples and has received widespread attention in recent years. Most existing IMVC methods still have the following issues that require to be further addressed. They focus solely on the first-order correlation information among samples, neglecting the more intricate high-order connections. Additionally, these methods always overlook the noise or inaccuracies in the self-representation matrix. To address above issues, a novel method named Robust Mixed-order Graph Learning (RMoGL) is proposed for IMVC. Specifically, to enhance the robustness to noise, the self-representation matrices are separated into clean graphs and noise graphs. To capture complex high-order relationships among samples, the dynamic high-order similarity graphs are innovatively constructed from the recovered data. The clean graphs are endowed with mixed-order information and tend towards to obtain a consensus graph via a self-weighted manner. An efficient algorithm based on Alternating Direction Method of Multipliers (ADMM) is designed to solve the proposed RMoGL, and superior performance is demonstrated by compared with nine state-of-the-art methods across eight datasets. The source code of this work is available at <span><span>https://github.com/guowei1314/RMoGL</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"115 ","pages":"Article 102776"},"PeriodicalIF":14.7,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Resolving multimodal ambiguity via knowledge-injection and ambiguity learning for multimodal sentiment analysis 通过知识注入和歧义学习解决多模态歧义，实现多模态情感分析

IF 14.7 1区计算机科学

Information Fusion Pub Date : 2024-10-31 DOI: 10.1016/j.inffus.2024.102745

Xianbing Zhao , Xuejiao Li , Ronghuan Jiang , Buzhou Tang

{"title":"Resolving multimodal ambiguity via knowledge-injection and ambiguity learning for multimodal sentiment analysis","authors":"Xianbing Zhao , Xuejiao Li , Ronghuan Jiang , Buzhou Tang","doi":"10.1016/j.inffus.2024.102745","DOIUrl":"10.1016/j.inffus.2024.102745","url":null,"abstract":"<div><div>Multimodal Sentiment Analysis (MSA) utilizes complementary multimodal features to predict sentiment polarity, which mainly involves language, vision, and audio modalities. Existing multimodal fusion methods primarily consider the complementarity of different modalities, while neglecting the ambiguity caused by conflicts between modalities (i.e. the text modality predicts positive sentiment while the visual modality predicts negative sentiment). To well diminish these conflicts, we develop a novel multimodal ambiguity learning framework, namely RMA, Resolving Multimodal Ambiguity via Knowledge-Injection and Ambiguity Learning for Multimodal Sentiment Analysis. Specifically, We introduce and filter external knowledge to enhance the consistency of cross-modal sentiment polarity prediction. Immediately, we explicitly measure ambiguity and dynamically adjust the impact between the subordinate modalities and the dominant modality to simultaneously consider the complementarity and conflicts of multiple modalities during multimodal fusion. Experiments demonstrate the dominantity of our proposed model across three public multimodal sentiment analysis datasets CMU-MOSI, CMU-MOSEI, and MELD.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"115 ","pages":"Article 102745"},"PeriodicalIF":14.7,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Natural language processing in finance: A survey 金融领域的自然语言处理：调查

IF 14.7 1区计算机科学

Information Fusion Pub Date : 2024-10-28 DOI: 10.1016/j.inffus.2024.102755

Kelvin Du , Yazhi Zhao , Rui Mao , Frank Xing , Erik Cambria

{"title":"Natural language processing in finance: A survey","authors":"Kelvin Du , Yazhi Zhao , Rui Mao , Frank Xing , Erik Cambria","doi":"10.1016/j.inffus.2024.102755","DOIUrl":"10.1016/j.inffus.2024.102755","url":null,"abstract":"<div><div>This survey presents an in-depth review of the transformative role of Natural Language Processing (NLP) in finance, highlighting its impact on ten major financial applications: (1) financial sentiment analysis, (2) financial narrative processing, (3) financial forecasting, (4) portfolio management, (5) question answering, virtual assistant and chatbot, (6) risk management, (7) regulatory compliance monitoring, (8) Environmental, Social, Governance (ESG) and sustainable finance, (9) explainable artificial intelligence (XAI) in finance and (10) NLP for digital assets. With the integration of vast amounts of unstructured financial data and advanced NLP techniques, the study explores how NLP enables data-driven decision-making and innovation in the financial sector, alongside the limitations and challenges. By providing a comprehensive analysis of NLP applications combining both academic and industrial perspectives, this study postulates the future trends and evolution of financial services. It introduces a unique review framework to understand the interaction of financial data and NLP technologies systematically and outlines the key drivers, transformations, and emerging areas in this field. This survey targets researchers, practitioners, and professionals, aiming to close their knowledge gap by highlighting the significance and future direction of NLP in enhancing financial services.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"115 ","pages":"Article 102755"},"PeriodicalIF":14.7,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Euclidean and Poincaré space ensemble Xgboost 欧几里得和庞加莱空间集合 Xgboost

IF 14.7 1区计算机科学

Information Fusion Pub Date : 2024-10-23 DOI: 10.1016/j.inffus.2024.102746

Ponnuthurai Nagaratnam Suganthan , Lingping Kong , Václav Snášel , Varun Ojha , Hussein Ahmed Hussein Zaky Aly

{"title":"Euclidean and Poincaré space ensemble Xgboost","authors":"Ponnuthurai Nagaratnam Suganthan , Lingping Kong , Václav Snášel , Varun Ojha , Hussein Ahmed Hussein Zaky Aly","doi":"10.1016/j.inffus.2024.102746","DOIUrl":"10.1016/j.inffus.2024.102746","url":null,"abstract":"<div><div>The Hyperbolic space has garnered attention for its unique properties and efficient representation of hierarchical structures. Recent studies have explored hyperbolic alternatives to hyperplane-based classifiers, such as logistic regression and support vector machines. Hyperbolic methods have even been fused into random forests by constructing data splits with horosphere, which proved effective for hyperbolic datasets. However, the existing incorporation of the horosphere leads to substantial computation time, diverting attention from its application on most datasets. Against this backdrop, we introduce an extension of Xgboost, a renowned machine learning (ML) algorithm to hyperbolic space, denoted as PXgboost. This extension involves a redefinition of the node split concept using the Riemannian gradient and Riemannian Hessian. Our findings unveil the promising performance of PXgboost compared to the algorithms in the literature through comprehensive experiments conducted on 64 datasets from the UCI ML repository and 8 datasets from WordNet by fusing both their Euclidean and hyperbolic-transformed (hyperbolic UCI) representations. Furthermore, our findings suggest that the Euclidean metric-based classifier performs well even on hyperbolic data. Building upon the above finding, we propose a space fusion classifier called, EPboost. It harmonizes data processing across various spaces and integrates probability outcomes for predictive analysis. In our comparative analysis involving 19 algorithms on the UCI dataset, our EPboost outperforms others in most cases, underscoring its efficacy and potential significance in diverse ML applications. This research marks a step forward in harnessing hyperbolic geometry for ML tasks and showcases its potential to enhance algorithmic efficacy.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"115 ","pages":"Article 102746"},"PeriodicalIF":14.7,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0