International Journal of Approximate Reasoning最新文献_第9页

Efficient parameter-free adaptive hashing for large-scale cross-modal retrieval 大规模跨模态检索的高效无参数自适应哈希

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-02-10 DOI: 10.1016/j.ijar.2025.109383

Bo Li , You Wu , Zhixin Li

{"title":"Efficient parameter-free adaptive hashing for large-scale cross-modal retrieval","authors":"Bo Li , You Wu , Zhixin Li","doi":"10.1016/j.ijar.2025.109383","DOIUrl":"10.1016/j.ijar.2025.109383","url":null,"abstract":"<div><div>The intention of deep cross-modal hashing retrieval (DCMHR) is to explore the connections between multi-media data, but most methods are only applicable to a few modalities and cannot be extended to other scenarios. Meanwhile, many methods also fail to emphasize the importance of unified training for classification loss and hash loss, which can also reduce the robustness and effectiveness of the model. Regarding these two issues, this paper designs Efficient Parameter-free Adaptive Hashing for Large-Scale Cross-Modal Retrieval (EPAH) to adaptively extract the modality variations and collect corresponding semantics of cross-modal features into the generated hash codes. EPAH does not use hyper-parameters, weight vectors, auxiliary matrices, and other structures to learn cross-modal data, while efficient parameter-free adaptive hashing can achieve multi-modal retrieval tasks. Specifically, our proposal is a two-stage strategy, divided into feature extraction and unified training, both stages are parameter-free adaptive learning. Meanwhile, this article simplifies the model training settings, selects the more stable gradient descent method, and designs the unified hash code generation function. Comprehensive experiments evidence that our EPAH approach can outperform the SoTA DCMHR methods. In addition, EPAH conducts the essential analysis of out-of-modality extension and parameter anti-interference, which demonstrates generalization and innovation. The code is available at <span><span>https://github.com/libo-02/EPAH</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"180 ","pages":"Article 109383"},"PeriodicalIF":3.2,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143395813","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

A logical formalisation of a hypothesis in weighted abduction: Towards user-feedback dialogues 加权溯因中假设的逻辑形式化：面向用户反馈对话

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-02-07 DOI: 10.1016/j.ijar.2025.109382

Shota Motoura, Ayako Hoshino, Itaru Hosomi, Kunihiko Sadamasa

{"title":"A logical formalisation of a hypothesis in weighted abduction: Towards user-feedback dialogues","authors":"Shota Motoura, Ayako Hoshino, Itaru Hosomi, Kunihiko Sadamasa","doi":"10.1016/j.ijar.2025.109382","DOIUrl":"10.1016/j.ijar.2025.109382","url":null,"abstract":"<div><div>Weighted abduction computes hypotheses that explain input observations. A reasoner of weighted abduction first generates possible hypotheses and then selects the hypothesis that is the most plausible. Since a reasoner employs parameters, called weights, that control its plausibility evaluation function, it can output the most plausible hypothesis according to a specific application using application-specific weights. This versatility makes it applicable from plant operation to cybersecurity or discourse analysis. However, the predetermined application-specific weights are not applicable to all cases of the application. Hence, the hypothesis selected by the reasoner does not necessarily seem the most plausible to the user. In order to resolve this problem, this article proposes two types of user-feedback dialogue protocols, in which the user points out, either positively, negatively or neutrally, properties of the hypotheses presented by the reasoner, and the reasoner regenerates hypotheses that satisfy the user's feedback. As it is required for user-feedback dialogue protocols, we then prove: (i) our protocols necessarily terminate under certain reasonable conditions; (ii) they converge on hypotheses that have the same properties in common as fixed target hypotheses do in common if the user determines the positivity, negativity or neutrality of each pointed-out property based on whether the target hypotheses have that property.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"179 ","pages":"Article 109382"},"PeriodicalIF":3.2,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377741","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

Trend-pattern unlimited fuzzy information granule-based LSTM model for long-term time-series forecasting 基于趋势模式无限模糊信息颗粒的长期时间序列预测LSTM模型

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-02-05 DOI: 10.1016/j.ijar.2025.109381

Yanan Jiang, Fusheng Yu, Yuqing Tang, Chenxi Ouyang, Fangyi Li

{"title":"Trend-pattern unlimited fuzzy information granule-based LSTM model for long-term time-series forecasting","authors":"Yanan Jiang, Fusheng Yu, Yuqing Tang, Chenxi Ouyang, Fangyi Li","doi":"10.1016/j.ijar.2025.109381","DOIUrl":"10.1016/j.ijar.2025.109381","url":null,"abstract":"<div><div>Trend fuzzy information granulation has shown promising results in long-term time-series forecasting and has attracted increasing attention. In the forecasting model based on trend fuzzy information granulation, the representation of trend granules plays a crucial role. The research focuses on developing trend granules and trend granular time series to effectively represent trend information and improve forecasting performance. However, the existing trend fuzzy information granulation methods make assumptions about the trend pattern of granules (i.e., assuming that granules have linear trends or definite nonlinear trends). Fuzzy information granules with presupposed trend patterns have limited expressive ability and struggle to capture complex nonlinear trends and temporal dependencies, thus limiting their forecasting performance. To address this issue, this paper proposes a novel kind of trend fuzzy information granules, named Trend-Pattern Unlimited Fuzzy Information Granules (TPUFIGs), which are constructed by the recurrent autoencoder with automatic feature learning and nonlinear modeling capabilities. Compared with the existing trend fuzzy information granules, TPUFIGs can better characterize potential trend patterns and temporal dependencies, and exhibit stronger robustness. With the TPUFIGs and Long Short-Term Memory (LSTM) neural network, we design the TPUFIG-LSTM forecasting model, which can effectively alleviate error accumulation and improve forecasting capability. Experimental results on six heterogeneous time series datasets demonstrate the superior performance of the proposed model. By combining deep learning and granular computing, this fuzzy information granulation method characterizes intricate dynamic features in time series more effectively, thus providing a novel solution for long-term time series forecasting with improved forecasting accuracy and generalization capability.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"180 ","pages":"Article 109381"},"PeriodicalIF":3.2,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143395812","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

The multi-criteria ranking method for criterion-oriented regret three-way decision 面向准则的后悔三向决策的多准则排序方法

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-01-31 DOI: 10.1016/j.ijar.2025.109374

Weidong Wan , Kai Zhang , Ligang Zhou

{"title":"The multi-criteria ranking method for criterion-oriented regret three-way decision","authors":"Weidong Wan , Kai Zhang , Ligang Zhou","doi":"10.1016/j.ijar.2025.109374","DOIUrl":"10.1016/j.ijar.2025.109374","url":null,"abstract":"<div><div>Recently, the criterion-oriented three-way decision has garnered widespread attention as it considers the decision-makers' preferences in handling multi-criteria decision-making problems. However, due to the fact that some criterion-oriented three-way decision models do not accurately consider the specific deviation between the object evaluation value and the criterion preference value when calculating the loss function, some of the objects show the weakness of ranking failure. In order to eliminate this weakness, this paper considers this deviation as the decision-maker's regret psychology, combines the regret theory, proposes a new loss function and constructs a new criterion-oriented regret three-way decision model. Firstly, an innovative approach for determining the loss function is introduced, integrating the decision-maker's basic demands with regret theory. Secondly, thresholds are derived by combining the decision-maker's basic demands with two optimization models. Thirdly, the <em>k</em>-means++ clustering algorithm is employed to derive the objects' fuzzy depictions. Then, this paper proposes a practical method for calculating conditional probabilities by combining the concept of closeness with the fuzzy depictions of the objects. Next, a multi-criteria ranking method founded on criterion-oriented regret three-way decision is proposed. Finally, the applicability of the innovative sequencing method is verified by combining parametric and comparative analyses for the computer hardware selection problem. Additionally, in dataset experiments, the proposed method is further validated on datasets containing known ranking results and datasets containing ordered classification.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"179 ","pages":"Article 109374"},"PeriodicalIF":3.2,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143093631","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

Reductions of concept lattices based on Boolean formal contexts 基于布尔形式语境的概念格约简

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-01-29 DOI: 10.1016/j.ijar.2025.109372

Dong-Yun Niu , Ju-Sheng Mi

引用次数: 0

Outlier detection in mixed-attribute data: A semi-supervised approach with fuzzy approximations and relative entropy 混合属性数据的离群点检测：一种模糊逼近和相对熵的半监督方法

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-01-29 DOI: 10.1016/j.ijar.2025.109373

Baiyang Chen , Zhong Yuan , Zheng Liu , Dezhong Peng , Yongxiang Li , Chang Liu , Guiduo Duan

{"title":"Outlier detection in mixed-attribute data: A semi-supervised approach with fuzzy approximations and relative entropy","authors":"Baiyang Chen , Zhong Yuan , Zheng Liu , Dezhong Peng , Yongxiang Li , Chang Liu , Guiduo Duan","doi":"10.1016/j.ijar.2025.109373","DOIUrl":"10.1016/j.ijar.2025.109373","url":null,"abstract":"<div><div>Outlier detection is a critical task in data mining, aimed at identifying objects that significantly deviate from the norm. Semi-supervised methods improve detection performance by leveraging partially labeled data but typically overlook the uncertainty and heterogeneity of real-world mixed-attribute data. This paper introduces a semi-supervised outlier detection method, namely fuzzy rough sets-based outlier detection (FROD), to effectively handle these challenges. Specifically, we first utilize a small subset of labeled data to construct fuzzy decision systems, through which we introduce the attribute classification accuracy based on fuzzy approximations to evaluate the contribution of attribute sets in outlier detection. Unlabeled data is then used to compute fuzzy relative entropy, which provides a characterization of outliers from the perspective of uncertainty. Finally, we develop the detection algorithm by combining attribute classification accuracy with fuzzy relative entropy. Experimental results on 16 public datasets show that FROD is comparable with or better than leading detection algorithms. All datasets and source codes are accessible at <span><span>https://github.com/ChenBaiyang/FROD</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"179 ","pages":"Article 109373"},"PeriodicalIF":3.2,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377742","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

Lifting factor graphs with some unknown factors for new individuals 对于新个体，带有未知因素的提升因子图

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-01-27 DOI: 10.1016/j.ijar.2025.109371

Malte Luttermann , Ralf Möller , Marcel Gehrke

引用次数: 0

PETS: Predicting efficiently using temporal symmetries in temporal probabilistic graphical models PETS：在时间概率图形模型中有效地使用时间对称性进行预测

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-01-23 DOI: 10.1016/j.ijar.2025.109370

Florian Andreas Marwitz, Ralf Möller, Marcel Gehrke

引用次数: 0

NeST: The neuro-symbolic transpiler NeST：神经符号转译器

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-01-22 DOI: 10.1016/j.ijar.2025.109369

Viktor Pfanschilling , Hikaru Shindo , Devendra Singh Dhami , Kristian Kersting

引用次数: 0

Controlling false positives in multiple instance learning: The “c-rule” approach 控制多实例学习中的误报：“c规则”方法

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-01-22 DOI: 10.1016/j.ijar.2025.109367

Rosario Delgado

{"title":"Controlling false positives in multiple instance learning: The “c-rule” approach","authors":"Rosario Delgado","doi":"10.1016/j.ijar.2025.109367","DOIUrl":"10.1016/j.ijar.2025.109367","url":null,"abstract":"<div><div>This paper introduces a novel strategy for labeling bags in binary Multiple Instance Learning (MIL) under the <em>standard MI assumption</em>. The proposed approach addresses errors in instance labeling by classifying a bag as positive if it contains at least <em>c</em> positively labeled instances. This strategy seeks to balance the trade-off between controlling the <em>false positive rate</em> (mislabeling a negative bag as positive) and the <em>false negative rate</em> (mislabeling a positive bag as negative) while reducing labeling efforts.</div><div>The study provides theoretical justifications for this approach and introduces algorithms for its implementation, including determining the minimum value of <em>c</em> required to keep error rates below predefined thresholds. Additionally, it proposes a methodology to estimate the number of genuinely positive and negative instances within bags. Simulations demonstrate the superior performance of the “<em>c</em>-rule” compared to the <em>standard</em> rule (corresponding to <span><math><mi>c</mi><mo>=</mo><mn>1</mn></math></span>) in scenarios with sparse positive bags and moderately low to high probability of misclassifying a negative instance. This trend is further validated through comparisons using two real-world datasets. Overall, this research advances the understanding of error management in MIL and provides practical tools for real-world applications.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"179 ","pages":"Article 109367"},"PeriodicalIF":3.2,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143093633","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