IEEE Transactions on Emerging Topics in Computational Intelligence最新文献

IEEE Computational Intelligence Society Information IEEE计算智能学会信息

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-07-23 DOI: 10.1109/TETCI.2025.3586976

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IEEE Transactions on Emerging Topics in Computational Intelligence Information for Authors IEEE计算智能信息新主题汇刊

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-07-23 DOI: 10.1109/TETCI.2025.3586974

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IEEE Transactions on Emerging Topics in Computational Intelligence Publication Information IEEE计算智能新兴主题汇刊

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-07-23 DOI: 10.1109/TETCI.2025.3586972

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Leveraging Neural Networks and Calibration Measures for Confident Feature Selection 利用神经网络和校准措施进行自信特征选择

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-04-14 DOI: 10.1109/TETCI.2025.3535659

Hassan Gharoun;Navid Yazdanjue;Mohammad Sadegh Khorshidi;Fang Chen;Amir H. Gandomi

{"title":"Leveraging Neural Networks and Calibration Measures for Confident Feature Selection","authors":"Hassan Gharoun;Navid Yazdanjue;Mohammad Sadegh Khorshidi;Fang Chen;Amir H. Gandomi","doi":"10.1109/TETCI.2025.3535659","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3535659","url":null,"abstract":"With the surge in data generation, both vertically (i.e., volume of data) and horizontally (i.e., dimensionality) the burden of the curse of dimensionality has become increasingly palpable. Feature selection, a key facet of dimensionality reduction techniques, has advanced considerably to address this challenge. One such advancement is the Boruta feature selection algorithm, which successfully discerns meaningful features by contrasting them to their permutated counterparts known as shadow features. Building on this, this paper introduces NeuroBoruta, that extends the traditional Boruta approach by integrating neural networks and calibration metrics to improve prediction accuracy and reduce model uncertainty. By augmenting shadow features with noise and utilizing neural network-based perturbation for importance evaluation, and further incorporating calibration metrics alongside accuracy this evolved version of the Boruta method is presented. Experimental results demonstrate that NeuroBoruta significantly enhances the predictive performance and reliability of classification models across various datasets, including medical imaging and standard UCI datasets. This study underscores the importance of considering both feature relevance and model uncertainty in the feature selection process, particularly in domains requiring high accuracy and reliability.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 3","pages":"2179-2193"},"PeriodicalIF":5.3,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10964323","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144148086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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A Survey of Multimodal Fake News Detection: A Cross-Modal Interaction Perspective 多模态假新闻检测研究：跨模态交互视角

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-04-09 DOI: 10.1109/TETCI.2025.3543389

Xianghua Li;Jiao Qiao;Shu Yin;Lianwei Wu;Chao Gao;Zhen Wang;Xuelong Li

{"title":"A Survey of Multimodal Fake News Detection: A Cross-Modal Interaction Perspective","authors":"Xianghua Li;Jiao Qiao;Shu Yin;Lianwei Wu;Chao Gao;Zhen Wang;Xuelong Li","doi":"10.1109/TETCI.2025.3543389","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3543389","url":null,"abstract":"The growth of social media platforms has made it easier for fake news to spread, which poses a significant threat to authoritative news outlets, politics, and public health. Manual verification of the massive amount of online information has proven to be a daunting task, which has led to the growing interest in automatic fake news detection. Some methods that rely on news text, images, external knowledge, social contexts, or propagation graphs have demonstrated good performance. In contrast to earlier studies that focused solely on the unimodal news textual information, recent works have integrated multimodal features from various granularities, such as words, visual semantic regions, and multimodal entities, to more effectively leverage news content and align with human reading habits. However, a comprehensive review of Multimodal Fake News Detection (MFND) is still lacking, prompting our aim to complement this topic. Specifically, we present a systematic taxonomy from the perspective of cross-modal interactions. We categorize existing methods into the data-based, entity-based, and knowledge-based approaches. Connections between various works are detailed when outlining representative papers. Additionally, we introduce prevalent multimodal learning methods, present accessible MFND datasets and evaluation metrics, and analyze current research results. Finally, the promising future research directions are discussed.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 4","pages":"2658-2675"},"PeriodicalIF":5.3,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144687742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Prompt-Based Out-of-Distribution Intent Detection 基于提示的分布外意图检测

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-03-27 DOI: 10.1109/TETCI.2024.3372440

Rudolf Chow;Albert Y. S. Lam

{"title":"Prompt-Based Out-of-Distribution Intent Detection","authors":"Rudolf Chow;Albert Y. S. Lam","doi":"10.1109/TETCI.2024.3372440","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3372440","url":null,"abstract":"Recent rapid advances in pre-trained language models, such as BERT and GPT, in natural language processing (NLP) have greatly improved the efficacy of text classifiers, easily surpassing human level performance in standard datasets like GLUE. However, most of these standard tasks implicitly assume a closed-world situation, where all testing data are supposed to lie in the same scope or distribution of the training data. Out-of-distribution (OOD) detection is the task of detecting when an input data point lies beyond the scope of the seen training set. This is becoming increasingly important as NLP agents, such as chatbots or virtual assistants, have been being deployed ubiquitously in our daily lives, thus attracting more attention from the research community to make it more accurate and robust at the same time. Recent work can be broadly categorized into two orthogonal approaches – data generative/augmentative methods and threshold/boundary learning. In this work, we follow the former and propose a method for the task based on prompting, which is known for its zero and few-shot capabilities. Generating synthetic outliers in terms of prompts allows the model to more efficiently learn OOD samples than the existing methods. Testing on nine different settings across three standard datasets used for OOD detection, our method with adaptive decision boundary is able to achieve competitive or superior performances compared with the current state-of-the-art in all cases. We also provide extensive analysis on each dataset as well as perform comprehensive ablation studies on each component of our model.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 3","pages":"2371-2382"},"PeriodicalIF":5.3,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144148162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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IEEE Computational Intelligence Society Information IEEE计算智能学会信息

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-03-27 DOI: 10.1109/TETCI.2025.3568244

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IEEE Transactions on Emerging Topics in Computational Intelligence Publication Information IEEE计算智能新兴主题汇刊

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-03-27 DOI: 10.1109/TETCI.2025.3568240

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IEEE Transactions on Emerging Topics in Computational Intelligence Information for Authors IEEE计算智能信息新主题汇刊

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-03-27 DOI: 10.1109/TETCI.2025.3568242

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IEEE Transactions on Emerging Topics in Computational Intelligence Information for Authors IEEE计算智能信息新主题汇刊

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-03-26 DOI: 10.1109/TETCI.2025.3548334

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