Information Sciences最新文献_第6页

PMformer: A novel informer-based model for accurate long-term time series prediction PMformer：基于信息的新型长期时间序列精确预测模型

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2024-10-23 DOI: 10.1016/j.ins.2024.121586

Yuewei Xue, Shaopeng Guan, Wanhai Jia

{"title":"PMformer: A novel informer-based model for accurate long-term time series prediction","authors":"Yuewei Xue, Shaopeng Guan, Wanhai Jia","doi":"10.1016/j.ins.2024.121586","DOIUrl":"10.1016/j.ins.2024.121586","url":null,"abstract":"<div><div>When applied to long-term time series forecasting, Informer struggles to capture temporal dependencies effectively, leading to suboptimal forecasting accuracy. To address this issue, we propose PMformer, a novel model based on Informer for long-term time series prediction. First, we introduce a probabilistic patch sampling attention mechanism that utilizes a patch-based strategy to compute attention scores within randomly selected sequence patches. This localized approach enhances the model's capability to capture local temporal dependencies, allowing it to better understand and process critical local features in time series while reducing computational complexity. Additionally, we propose a multi-scale scaling sparse attention technique that balances attention distribution by combining coarse- and fine-grained attention scores, thereby improving the model's ability to capture global sequence information. Finally, we design a dilated causal pooling layer and a multilayer perceptual cross self-attention decoder to further enhance the model's prediction accuracy by capturing key information in long-term correlations and precisely focusing on sequences. We conducted experiments on both multivariate and univariate time series forecasting tasks. The results show that PMformer outperforms six baseline models, including PatchTST and FEDformer, in terms of MAE and MSE metrics. This demonstrates its superior ability to capture temporal dependencies, achieving more accurate predictions.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"690 ","pages":"Article 121586"},"PeriodicalIF":8.1,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530662","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

TS-MAE: A masked autoencoder for time series representation learning TS-MAE：用于时间序列表示学习的掩码自动编码器

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2024-10-23 DOI: 10.1016/j.ins.2024.121576

Qian Liu , Junchen Ye , Haohan Liang , Leilei Sun , Bowen Du

{"title":"TS-MAE: A masked autoencoder for time series representation learning","authors":"Qian Liu , Junchen Ye , Haohan Liang , Leilei Sun , Bowen Du","doi":"10.1016/j.ins.2024.121576","DOIUrl":"10.1016/j.ins.2024.121576","url":null,"abstract":"<div><div>Self-supervised learning (SSL) has been widely researched in recent years. In Particular, generative self-supervised learning methods have achieved remarkable success in many AI domains, such as MAE in computer vision, well-known BERT, GPT in natural language processing, and GraphMAE in graph learning. However, in the context of time series analysis, not only is the work that follows this line limited but also the performance has not reached the potential as promised in other fields. To fill this gap, we propose a simple and elegant masked autoencoder for time series representation learning. Firstly, unlike most existing work which uses the Transformer as the backbone, we build our model based on neural ordinary differential equation which possesses excellent mathematical properties. Compared with the position encoding in Transformer, modeling the evolution patterns continuously could better extract the temporal dependency. Secondly, a timestamp-wise mask strategy is provided to cooperate with the autoencoder to avoid bias, and it also could reduce the cross-imputation between variables to learn more robust representations. Lastly, extensive experiments conducted on two classical tasks demonstrate the superiority of our model over the state-of-the-art ones.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"690 ","pages":"Article 121576"},"PeriodicalIF":8.1,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142538570","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

Light sensor based covert channels on mobile devices 基于光传感器的移动设备隐蔽信道

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2024-10-23 DOI: 10.1016/j.ins.2024.121581

Mila Dalla Preda , Claudia Greco , Michele Ianni , Francesco Lupia , Andrea Pugliese

引用次数: 0

Unveiling diagnostic information for type 2 diabetes through interpretable machine learning 通过可解释的机器学习揭示 2 型糖尿病的诊断信息

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2024-10-22 DOI: 10.1016/j.ins.2024.121582

Xiang Lv , Jiesi Luo , Yonglin Zhang , Hui Guo , Ming Yang , Menglong Li , Qi Chen , Runyu Jing

{"title":"Unveiling diagnostic information for type 2 diabetes through interpretable machine learning","authors":"Xiang Lv , Jiesi Luo , Yonglin Zhang , Hui Guo , Ming Yang , Menglong Li , Qi Chen , Runyu Jing","doi":"10.1016/j.ins.2024.121582","DOIUrl":"10.1016/j.ins.2024.121582","url":null,"abstract":"<div><div>The interpretability of disease prediction models is often crucial for their trustworthiness and usability among medical practitioners. Existing methods in interpretable artificial intelligence improve model transparency but fall short in identifying precise, disease-specific primal information. In this work, an interpretable deep learning-based algorithm called the data space landmark refiner was developed, which not only enhances both global interpretability and local interpretability but also reveals the intrinsic information of the data distribution. Using the proposed method, a type 2 diabetes mellitus diagnostic model with high interpretability was constructed on the basis of the electronic health records from two hospitals. Moreover, effective diagnostic information was directly derived from the model’s internal parameters, demonstrating strong alignment with current clinical knowledge. Compared with conventional interpretable machine learning approaches, the proposed method offered more precise and specific interpretability, increasing clinical practitioners’ trust in machine learning-supported diagnostic models.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"690 ","pages":"Article 121582"},"PeriodicalIF":8.1,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Discounted fully probabilistic design of decision rules 决策规则的全概率贴现设计

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2024-10-22 DOI: 10.1016/j.ins.2024.121578

Miroslav Kárný, Soňa Molnárová

{"title":"Discounted fully probabilistic design of decision rules","authors":"Miroslav Kárný, Soňa Molnárová","doi":"10.1016/j.ins.2024.121578","DOIUrl":"10.1016/j.ins.2024.121578","url":null,"abstract":"<div><div>Axiomatic fully probabilistic design (FPD) of optimal decision rules strictly extends the decision making (DM) theory represented by Markov decision processes (MDP). This means that any MDP task can be approximated by an explicitly found FPD task whereas many FPD tasks have no MDP equivalent. MDP and FPD model the closed loop — the coupling of an agent and its environment — via a joint probability density (pd) relating the involved random variables, referred to as behaviour. Unlike MDP, FPD quantifies agent's aims and constraints by an <em>ideal pd</em>. The ideal pd is high on the desired behaviours, small on undesired behaviours and zero on forbidden ones. FPD selects the optimal decision rules as the minimiser of Kullback-Leibler's divergence of the closed-loop-modelling pd to its ideal twin. The proximity measure choice follows from the FPD axiomatics.</div><div>MDP minimises the expected total loss, which is usually the sum of discounted partial losses. The discounting reflects the decreasing importance of future losses. It also diminishes the influence of errors caused by:</div><div><figure><img></figure> the imperfection of the employed environment model;</div><div><figure><img></figure> roughly-expressed aims;</div><div><figure><img></figure> the approximate learning and decision-rules design.</div><div>The established FPD cannot currently account for these important features. The paper elaborates the missing discounted version of FPD. This non-trivial filling of the gap in FPD also employs an extension of dynamic programming, which is of an independent interest.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"690 ","pages":"Article 121578"},"PeriodicalIF":8.1,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530657","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

Evidence combination with multi-granularity belief structure for pattern classification 利用多粒度信念结构进行证据组合以实现模式分类

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2024-10-22 DOI: 10.1016/j.ins.2024.121577

Kezhu Zuo , Xinde Li , Le Yu , Tao Shen , Yilin Dong , Jean Dezert

引用次数: 0

Decomposition of pseudo-uninorms with continuous underlying functions via ordinal sum 通过序数和分解具有连续基础函数的伪无穷级数

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2024-10-22 DOI: 10.1016/j.ins.2024.121573

Juraj Kalafut , Andrea Mesiarová-Zemánková

引用次数: 0

Improving two-dimensional linear discriminant analysis with L1 norm for optimizing EEG signal 利用 L1 准则改进二维线性判别分析，优化脑电信号

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2024-10-22 DOI: 10.1016/j.ins.2024.121585

Bin Lu , Fuwang Wang , Junxiang Chen , Guilin Wen , Rongrong Fu

{"title":"Improving two-dimensional linear discriminant analysis with L1 norm for optimizing EEG signal","authors":"Bin Lu , Fuwang Wang , Junxiang Chen , Guilin Wen , Rongrong Fu","doi":"10.1016/j.ins.2024.121585","DOIUrl":"10.1016/j.ins.2024.121585","url":null,"abstract":"<div><div>Dimensionality reduction is a critical factor in processing high-dimensional datasets. The L1 norm-based Two-Dimensional Linear Discriminant Analysis (L1-2DLDA) is widely used for this purpose, but it remains sensitive to outliers and classes with large deviations, which deteriorates its performance. To address this limitation, the present study proposed Pairwise Sample Distance Two-Dimensional Linear Discriminant Analysis (PSD2DLDA), a novel method that modeled L1-2DLDA using pair-wise sample distances. To improve computational effectiveness, this study also introduced a streamlined variant, Pairwise Class Mean Distance Two-Dimensional Linear Discriminant Analysis (PCD2DLDA), which was based on distances between class mean pairs. Different from previous studies, this study utilized the projected sub-gradient method to optimize these two improved methods. Meanwhile, this study explored the interrelationship, limitations, and applicability of these two improved methods. The comparative experimental results on three datasets validated the outstanding performance of PSD2DLDA and PCD2DLDA methods. In particular, PSD2DLDA exhibited superior robustness compared to PCD2DLDA. Furthermore, applying these two methods to optimize electroencephalogram (EEG) signals effectively enhanced the decoding accuracy of motor imagery neural patterns, which offered a promising strategy for optimizing EEG signals processing in brain-computer interface (BCI) applications.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"690 ","pages":"Article 121585"},"PeriodicalIF":8.1,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142538694","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

Efficiency analysis in bi-level on fuzzy input and output 关于模糊输入和输出的双层效率分析

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2024-10-22 DOI: 10.1016/j.ins.2024.121551

Kh. Ghaziyani , F. Hosseinzadeh Lotfi , Sohrab Kordrostami , Alireza Amirteimoori

{"title":"Efficiency analysis in bi-level on fuzzy input and output","authors":"Kh. Ghaziyani , F. Hosseinzadeh Lotfi , Sohrab Kordrostami , Alireza Amirteimoori","doi":"10.1016/j.ins.2024.121551","DOIUrl":"10.1016/j.ins.2024.121551","url":null,"abstract":"<div><div>To enhance the conventional framework of data envelope analysis (DEA), a novel hybrid bi-level model is proposed, integrating fuzzy logic with triangular fuzzy numbers to effectively address data uncertainty. This model innovatively departs from the traditional DEA’s ’black box’ approach by incorporating inter-organizational relationships and the internal dynamics of decision-making units (DMUs). Utilizing a modified Russell’s method, it provides a nuanced efficiency analysis in scenarios of ambiguous data. The study aims to enhance the accuracy and applicability of Data Envelopment Analysis in uncertain data environments. To achieve this, a novel hybrid bi-level model integrating fuzzy logic is presented. Validated through a case study involving 15 branches of a private Iranian bank, the model demonstrates improved accuracy in efficiency assessments and paves the way for future research in operational systems uncertainty management. The results indicated that, among the 15 branches of a private Iranian bank analyzed for the year 2022, branches 1, 10, and 11 demonstrated leader-level efficiency, while branch 3 exhibited follower-level efficiency, and branch 1 achieved overall efficiency. These branches attained an efficiency rating of <span><math><mrow><msup><mi>E</mi><mrow><mo>+</mo><mo>+</mo></mrow></msup></mrow></math></span>, signifying a high level of efficiency within the model’s parameters.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"690 ","pages":"Article 121551"},"PeriodicalIF":8.1,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530615","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

GKF-PUAL: A group kernel-free approach to positive-unlabeled learning with variable selection GKF-PUAL：带变量选择的无组核正向无标记学习方法

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2024-10-22 DOI: 10.1016/j.ins.2024.121574

Xiaoke Wang , Rui Zhu , Jing-Hao Xue

引用次数: 0