IEEE Transactions on Emerging Topics in Computational Intelligence最新文献

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}

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}

引用次数: 0

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

引用次数: 0

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

引用次数: 0

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

引用次数: 0

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

引用次数: 0

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 : 2025-03-26 DOI: 10.1109/TETCI.2025.3548330

引用次数: 0

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 : 2025-03-26 DOI: 10.1109/TETCI.2025.3548332

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PurifyFL: Non-Interactive Privacy-Preserving Federated Learning Against Poisoning Attacks Based on Single Server PurifyFL：针对单服务器中毒攻击的非交互式隐私保护联邦学习

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-03-17 DOI: 10.1109/TETCI.2025.3540420

Yanli Ren;Zhe Yang;Guorui Feng;Xinpeng Zhang

引用次数: 0

A Learning-Based Two-Stage Multi-Thread Iterated Greedy Algorithm for Co-Scheduling of Distributed Factories and Automated Guided Vehicles With Sequence-Dependent Setup Times 一种基于学习的两阶段多线程迭代贪心算法用于序列依赖的分布式工厂和自动导引车协同调度

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-03-14 DOI: 10.1109/TETCI.2025.3540405

Zijiang Liu;Hongyan Sang;Biao Zhang;Leilei Meng;Tao Meng

{"title":"A Learning-Based Two-Stage Multi-Thread Iterated Greedy Algorithm for Co-Scheduling of Distributed Factories and Automated Guided Vehicles With Sequence-Dependent Setup Times","authors":"Zijiang Liu;Hongyan Sang;Biao Zhang;Leilei Meng;Tao Meng","doi":"10.1109/TETCI.2025.3540405","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3540405","url":null,"abstract":"Automated guided vehicles are widely utilized in the real production environment for tasks such as job transfer and inter-factory collaboration, yet they remain relatively underexplored in academic research. This study addresses the distributed permutation flow shop co-scheduling problem with sequence-dependent setup times (DPFCSP-SDST). We propose a novel solution that leverages an optimization algorithm, specifically a learning-based two-stage multi-thread iterated greedy algorithm (LTMIG). First, a problem-specific initialization method is designed to generate the initialization solution in two stages. Second, a Q-learning-based operator adaptation strategy is adopted to guide the evolutionary direction of factory assignment to reduce the makespan. Then, the proposed destructive-construction strategy builds an archive set to share historical knowledge with different stages of search, ensuring exploration capability. Local search effectively combines the parallel computing power of multi-threading with the inherent exploitation capability of LTMIG, and fully utilizes the information of elite solutions. Extensive experimental results demonstrate that LTMIG is significantly better than the comparison algorithms mentioned in the paper, and it turns out that LTMIG is the most suitable algorithm for solving DPFCSP-SDST.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 3","pages":"2208-2218"},"PeriodicalIF":5.3,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144148133","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