International Journal of Approximate Reasoning最新文献_第5页

Efficient parameter-free adaptive hashing for large-scale cross-modal retrieval

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

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

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

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

Global sensitivity analysis of uncertain parameters in Bayesian networks

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.2025.109368

Rafael Ballester-Ripoll, Manuele Leonelli

{"title":"Global sensitivity analysis of uncertain parameters in Bayesian networks","authors":"Rafael Ballester-Ripoll, Manuele Leonelli","doi":"10.1016/j.ijar.2025.109368","DOIUrl":"10.1016/j.ijar.2025.109368","url":null,"abstract":"<div><div>Traditionally, the sensitivity analysis of a Bayesian network studies the impact of individually modifying the entries of its conditional probability tables in a one-at-a-time (OAT) fashion. However, this approach fails to give a comprehensive account of each inputs' relevance, since simultaneous perturbations in two or more parameters often entail higher-order effects that cannot be captured by an OAT analysis. We propose to conduct global variance-based sensitivity analysis instead, whereby <em>n</em> parameters are viewed as uncertain at once and their importance is assessed jointly. Our method works by encoding the uncertainties as <em>n</em> additional variables of the network. To prevent the curse of dimensionality while adding these dimensions, we use low-rank tensor decomposition to break down the new potentials into smaller factors. Last, we apply the method of Sobol to the resulting network to obtain <em>n</em> global sensitivity indices, one for each parameter of interest. Using a benchmark array of both expert-elicited and learned Bayesian networks, we demonstrate that the Sobol indices can significantly differ from the OAT indices, thus revealing the true influence of uncertain parameters and their interactions.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"180 ","pages":"Article 109368"},"PeriodicalIF":3.2,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143395814","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

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

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

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

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

Fuzzy rough set attribute reduction based on decision ball model

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.2025.109364

Xia Ji , Wanyu Duan , Jianhua Peng , Sheng Yao

{"title":"Fuzzy rough set attribute reduction based on decision ball model","authors":"Xia Ji , Wanyu Duan , Jianhua Peng , Sheng Yao","doi":"10.1016/j.ijar.2025.109364","DOIUrl":"10.1016/j.ijar.2025.109364","url":null,"abstract":"<div><div>Attribute reduction is a crucial step in data preprocessing in the field of data mining. Accurate measurement of the classification ability of attribute sets stands a central issue in attribute reduction research. The existing fuzzy rough set attribute reduction algorithms measure the classification ability of attribute sets by evaluating the proximity between fuzzy similarity classes and decision classes. However, the granularity of the decision class is too large to reflect the data distribution within the decision class, which may lead to misclassification of samples, thus affecting the effectiveness of attribute reduction. To address this problem, we refine the decision class to propose the concept of decision ball, and study a new extended fuzzy rough set model based on decision ball. In this model, decision balls serve as the evaluation granularity, facilitating the fitting of data distributions and measuring the classification ability of attributes. Expanding on this foundation, we have designed a fuzzy rough set attribute reduction algorithm based on decision ball model (DBFRS). We conducted extensive comparative experiments involving 9 state-of-the-art attribute reduction algorithms on 18 public datasets. Experimental results demonstrate that DBFRS attains high classification accuracy. Moreover, DBFRS exhibits better reduction performance on large and high-dimensional datasets. Compared to current fuzzy rough set methods, DBFRS demonstrates better applicability.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"179 ","pages":"Article 109364"},"PeriodicalIF":3.2,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143093630","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