Intelligent Data Analysis最新文献_第2页

Robust partial face recognition using multi-label attributes 使用多标签属性进行稳健的部分人脸识别

IF 1.7 4区计算机科学

Intelligent Data Analysis Pub Date : 2023-11-30 DOI: 10.3233/ida-227309

Gaoli Sang, Dan Zeng, Chao Yan, Raymond Veldhuis, Luuk Spreeuwers

{"title":"Robust partial face recognition using multi-label attributes","authors":"Gaoli Sang, Dan Zeng, Chao Yan, Raymond Veldhuis, Luuk Spreeuwers","doi":"10.3233/ida-227309","DOIUrl":"https://doi.org/10.3233/ida-227309","url":null,"abstract":"Partial face recognition (PFR) is challenging as the appearance of the face changes significantly with occlusion. In particular, these occlusions can be due to any item and may appear in any position that seriously hinders the extraction of discriminative features. Existing methods deal with PFR either by training a deep model with existing face databases containing limited occlusion types or by extracting un-occluded features directly from face regions without occlusions. Limited training data (i.e., occlusion type and diversity) can not cover the real-occlusion situations, and thus training-based methods can not learn occlusion robust discriminative features. The performance of occlusion region-based method is bounded by occlusion detection. Different from limited training data and occlusion region-based methods, we propose to use multi-label attributes for Partial Face Recognition (Attr4PFR). A novel data augmentation is proposed to solve limited training data and generate occlusion attributes. Apart from occlusion attributes, we also include soft biometric attributes and semantic attributes to explore more rich attributes to combat the loss caused by occlusions. To train our Attr4PFR, we propose an implicit attributes loss combined with a softmax loss to enforce Attr4PFR to learn discriminative features. As multi-label attributes are our auxiliary signal in the training phase, we do not need them in the inference. Extensive experiments on public benchmark AR and IJB-C databases show our method is 3% and 2.3% improvement compared to the state-of-the-art.","PeriodicalId":50355,"journal":{"name":"Intelligent Data Analysis","volume":"7 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139206284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Aggregating knowledge and collaborative information for sequential recommendation 汇聚知识和协作信息，实现按序推荐

IF 1.7 4区计算机科学

Intelligent Data Analysis Pub Date : 2023-11-30 DOI: 10.3233/ida-227198

Yunqi Zhang, Jidong Yuan, Chixuan Wei, Yifei Xie

{"title":"Aggregating knowledge and collaborative information for sequential recommendation","authors":"Yunqi Zhang, Jidong Yuan, Chixuan Wei, Yifei Xie","doi":"10.3233/ida-227198","DOIUrl":"https://doi.org/10.3233/ida-227198","url":null,"abstract":"Sequential recommendation aims to predict users’ future activities based on their historical interaction sequences. Various neural network architectures, such as Recurrent Neural Networks (RNN), Graph Neural Networks (GNN), and self-attention mechanisms, have been employed in the tasks, exploring multiple aspects of user preferences, including general interests, short-term interests, long-term interests, and item co-occurrence patterns. Despite achieving good performance, there are still limitations in capturing complex user preferences. Specifically, the current structures of RNN, GNN, etc., only capture item-level transition relations while neglecting attribute-level transition relations. Additionally, the explicit item relations are studied using item co-occurrence modules, but they cannot capture the implicit item-item relations. To address these issues, we propose a knowledge-augmented Gated Recurrent Unit (GRU) to improve the short-term user interest module and adopt a collaborative item aggregation method to enhance the item co-occurrence module. Additionally, our long-term interest module utilizes a bitwise gating mechanism to select historical item features significant to users’ current preferences. We extensively evaluate our model on three real-world datasets alongside competitive methods, demonstrating its effectiveness in top K sequential recommendation.","PeriodicalId":50355,"journal":{"name":"Intelligent Data Analysis","volume":"43 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139207686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Supervised probabilistic latent semantic analysis with applications to controversy analysis of legislative bills 有监督的概率潜在语义分析在立法议案争议分析中的应用

IF 1.7 4区计算机科学

Intelligent Data Analysis Pub Date : 2023-11-27 DOI: 10.3233/ida-227202

Eyor Alemayehu, Yi Fang

{"title":"Supervised probabilistic latent semantic analysis with applications to controversy analysis of legislative bills","authors":"Eyor Alemayehu, Yi Fang","doi":"10.3233/ida-227202","DOIUrl":"https://doi.org/10.3233/ida-227202","url":null,"abstract":"Probabilistic Latent Semantic Analysis (PLSA) is a fundamental text analysis technique that models each word in a document as a sample from a mixture of topics. PLSA is the precursor of probabilistic topic models including Latent Dirichlet Allocation (LDA). PLSA, LDA and their numerous extensions have been successfully applied to many text mining and retrieval tasks. One important extension of LDA is supervised LDA (sLDA), which distinguishes itself from most topic models in that it is supervised. However, to the best of our knowledge, no prior work extends PLSA in a similar manner sLDA extends LDA by jointly modeling the contents and the responses of documents. In this paper, we propose supervised PLSA (sPLSA) which can efficiently infer latent topics and their factorized response values from the contents and the responses of documents. The major challenge lies in estimating a document’s topic distribution which is a constrained probability that is dictated by both the content and the response of the document. To tackle this challenge, we introduce an auxiliary variable to transform the constrained optimization problem to an unconstrained optimization problem. This allows us to derive an efficient Expectation and Maximization (EM) algorithm for parameter estimation. Compared to sLDA, sPLSA converges much faster and requires less hyperparameter tuning, while performing similarly on topic modeling and better in response factorization. This makes sPLSA an appealing choice for latent response analysis such as ranking latent topics by their factorized response values. We apply the proposed sPLSA model to analyze the controversy of bills from the United States Congress. We demonstrate the effectiveness of our model by identifying contentious legislative issues.","PeriodicalId":50355,"journal":{"name":"Intelligent Data Analysis","volume":"220 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139232948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

TSAGNN: Temporal link predict method based on two stream adaptive graph neural network TSAGNN：基于双流自适应图神经网络的时态链接预测方法

IF 1.7 4区计算机科学

Intelligent Data Analysis Pub Date : 2023-11-25 DOI: 10.3233/ida-237367

Yuhang Zhu, Jing Guo, Haitao Li, Shuxin Liu, Yingle Li

{"title":"TSAGNN: Temporal link predict method based on two stream adaptive graph neural network","authors":"Yuhang Zhu, Jing Guo, Haitao Li, Shuxin Liu, Yingle Li","doi":"10.3233/ida-237367","DOIUrl":"https://doi.org/10.3233/ida-237367","url":null,"abstract":"Temporal link prediction based on graph neural networks has become a hot spot in the field of complex networks. To solve the problems of the existing temporal link prediction methods based on graph neural networks do not consider the future time-domain features and spatial-domain features are limited used, this paper proposes a novel temporal link prediction method based on two streams adaptive graph neural networks. Firstly, the network topology features are extracted from the micro, meso, and middle perspectives. Combined with the adaptive mechanism of convolution and self-attention, the preprocessing of the feature extraction is more effective; Secondly, an extended bi-directional long short-term memory network is proposed, which uses graph convolution to process topological features, and recursively learns the state vectors of the target snapshot by using the future time-domain information and the past historical information; Thirdly, the location coding is replaced by the time-coding for the transformer mechanism, so that past information and future information can be learned from each other, and the time-domain information of the network can be further mined; Finally, a novel two-stream network framework is proposed, which combines the processing results of point features and edge features. The experimental results on 9 data sets show that the proposed method has a better prediction effect and better robustness than the classical graph neural network methods.","PeriodicalId":50355,"journal":{"name":"Intelligent Data Analysis","volume":"26 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139236440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

How graph features from message passing affect graph classification and regression? 消息传递的图形特征如何影响图形分类和回归？

IF 1.7 4区计算机科学

Intelligent Data Analysis Pub Date : 2023-11-25 DOI: 10.3233/ida-227190

Masatsugu Yamada, Mahito Sugiyama

{"title":"How graph features from message passing affect graph classification and regression?","authors":"Masatsugu Yamada, Mahito Sugiyama","doi":"10.3233/ida-227190","DOIUrl":"https://doi.org/10.3233/ida-227190","url":null,"abstract":"Graph neural networks (GNNs) have been applied to various graph domains. However, GNNs based on the message passing scheme, which iteratively aggregates information from neighboring nodes, have difficulty learning to represent larger subgraph structures because of the nature of the scheme. We investigate the prediction performance of GNNs when the number of message passing iteration increases to capture larger subgraph structures on classification and regression tasks using various real-world graph datasets. Our empirical results show that the averaged features over nodes obtained by the message passing scheme in GNNs are likely to converge to a certain value, which significantly deteriorates the resulting prediction performance. This is in contrast to the state-of-the-art Weisfeiler–Lehman graph kernel, which has been used actively in machine learning for graphs, as it can comparably learn the large subgraph structures and its performance does not usually drop significantly drop from the first couple of rounds of iterations. Moreover, we report that when we apply node features obtained via GNNs to SVMs, the performance of the Weisfeiler-Lehman kernel can be superior to that of the graph convolutional model, which is a typically employed approach in GNNs.","PeriodicalId":50355,"journal":{"name":"Intelligent Data Analysis","volume":"19 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139236591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Evolutionary feature selection based on hybrid bald eagle search and particle swarm optimization 基于混合秃鹰搜索和粒子群优化的进化特征选择

IF 1.7 4区计算机科学

Intelligent Data Analysis Pub Date : 2023-11-25 DOI: 10.3233/ida-227222

Zhao Liu, Aimin Wang, Geng Sun, Jiahui Li, Haiming Bao, Yanheng Liu

{"title":"Evolutionary feature selection based on hybrid bald eagle search and particle swarm optimization","authors":"Zhao Liu, Aimin Wang, Geng Sun, Jiahui Li, Haiming Bao, Yanheng Liu","doi":"10.3233/ida-227222","DOIUrl":"https://doi.org/10.3233/ida-227222","url":null,"abstract":"Feature selection is a complicated multi-objective optimization problem with aims at reaching to the best subset of features while remaining a high accuracy in the field of machine learning, which is considered to be a difficult task. In this paper, we design a fitness function to jointly optimize the classification accuracy and the selected features in the linear weighting manner. Then, we propose two hybrid meta-heuristic methods which are the hybrid basic bald eagle search-particle swarm optimization (HBBP) and hybrid chaos-based bald eagle search-particle swarm optimization (HCBP) that alleviate the drawbacks of bald eagle search (BES) by utilizing the advantages of particle swarm optimization (PSO) to efficiently optimize the designed fitness function. Specifically, HBBP is proposed to overcome the disadvantages of the originals (i.e., BES and PSO) and HCBP is proposed to further improve the performance of HBBP. Moreover, a binary optimization is utilized to effectively transfer the solution space from continuous to binary. To evaluate the effectiveness, 17 well-known data sets from the UCI repository are employed as well as a set of well-established algorithms from the literature are adopted to jointly confirm the effectiveness of the proposed methods in terms of fitness value, classification accuracy, computational time and selected features. The results support the superiority of the proposed hybrid methods against the basic optimizers and the comparative algorithms on the most tested data sets.","PeriodicalId":50355,"journal":{"name":"Intelligent Data Analysis","volume":"64 9","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139237718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 review on network representation learning with multi-granularity perspective 多粒度视角下的网络表征学习综述

IF 1.7 4区计算机科学

Intelligent Data Analysis Pub Date : 2023-11-22 DOI: 10.3233/ida-227328

Shun Fu, Lufeng Wang, Jie Yang

{"title":"A review on network representation learning with multi-granularity perspective","authors":"Shun Fu, Lufeng Wang, Jie Yang","doi":"10.3233/ida-227328","DOIUrl":"https://doi.org/10.3233/ida-227328","url":null,"abstract":"Network data is ubiquitous, such as telecommunication, transport systems, online social networks, protein-protein interactions, etc. Since the huge scale and the complexity of network data, former machine learning system tried to understand network data arduously. On the other hand, thought of multi-granular cognitive computation simulates the problem-solving process of human brains. It simplifies the complex problems and solves problems from the easier to harder. Therefore, the application of multi-granularity problem-solving ideas or methods to deal with network data mining is increasingly adopted by researchers either intentionally or unintentionally. This paper looks into the domain of network representation learning (NRL). It systematically combs the research work in this field in recent years. In this paper, it is discovered that in dealing with the complexity of the network and pursuing the efficiency of computing resources, the multi-granularity solution becomes an excellent path that is hard to go around. Although there are several papers about survey of NRL, to our best knowledge, we are the first to survey the NRL from the perspective of multi-granular computing. This paper proposes the challenges that NRL meets. Furthermore, the feasibility of solving the challenges of NRL with multi-granular computing methodologies is analyzed and discussed. Some potential key scientific problems are sorted out and prospected in applying multi-granular computing for NRL research.","PeriodicalId":50355,"journal":{"name":"Intelligent Data Analysis","volume":"237 ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139249002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Using eigenvalues of distance matrices for outlier detection 利用距离矩阵的特征值检测离群值

IF 1.7 4区计算机科学

Intelligent Data Analysis Pub Date : 2023-11-21 DOI: 10.3233/ida-230048

Reza Modarres

引用次数: 0

Multi-head attention based candidate segment selection in QA over hybrid data 混合数据质量保证中基于多头注意力的候选段选择

IF 1.7 4区计算机科学

Intelligent Data Analysis Pub Date : 2023-11-20 DOI: 10.3233/ida-227032

Qian Chen, Xiaoying Gao, Xin Guo, Suge Wang

{"title":"Multi-head attention based candidate segment selection in QA over hybrid data","authors":"Qian Chen, Xiaoying Gao, Xin Guo, Suge Wang","doi":"10.3233/ida-227032","DOIUrl":"https://doi.org/10.3233/ida-227032","url":null,"abstract":"Question Answering based on Tabular and Textual data is a novel task proposed in recent years in the field of QA. At present, most QA systems return answers from a single data form, such as knowledge graphs, tables, texts. However, hybrid data including structured and unstructured data is quite pervasive in real life instead of a single form. Recent research on TAT-QA mainly suffers from the higher error of extracting supporting evidences from both tabular and textual content. This paper aimed to address the problem of failure evidence extraction from more complex and realistic hybrid data. We first proposed two types of metrics to evaluate the performance of evidence extraction on hybrid data, i.e. wrong evidence ratio (WER) and missing evidence ratio (MER). Then we utilize a candidate extractor to obtain supporting evidence related to the question. Third, an origin selector is designed to determine from where the question’s answer comes. Finally, the loss of origin selector is fused to the final loss function, which can improve the evidence extraction performance. Experimental results on the TAT-QA dataset showed that our proposed model outperforms the best baseline in terms of F1, WER and MER, which proves the effectiveness of our model.","PeriodicalId":50355,"journal":{"name":"Intelligent Data Analysis","volume":"42 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139255260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Effectiveness of ELMo embeddings, and semantic models in predicting review helpfulness ELMo 嵌入和语义模型在预测评论有用性方面的效果

IF 1.7 4区计算机科学

Intelligent Data Analysis Pub Date : 2023-11-16 DOI: 10.3233/ida-230349

Muhammad Shahid Iqbal Malik, Aftab Nawaz, M. Jamjoom, Dmitry I. Ignatov

{"title":"Effectiveness of ELMo embeddings, and semantic models in predicting review helpfulness","authors":"Muhammad Shahid Iqbal Malik, Aftab Nawaz, M. Jamjoom, Dmitry I. Ignatov","doi":"10.3233/ida-230349","DOIUrl":"https://doi.org/10.3233/ida-230349","url":null,"abstract":"Online product reviews (OPR) are a commonly used medium for consumers to communicate their experiences with products during online shopping. Previous studies have investigated the helpfulness of OPRs using frequency-based, linguistic, meta-data, readability, and reviewer attributes. In this study, we explored the impact of robust contextual word embeddings, topic, and language models in predicting the helpfulness of OPRs. In addition, the wrapper-based feature selection technique is employed to select effective subsets from each type of features. Five feature generation techniques including word2vec, FastText, Global Vectors for Word Representation (GloVe), Latent Dirichlet Allocation (LDA), and Embeddings from Language Models (ELMo), were employed. The proposed framework is evaluated on two Amazon datasets (Video games and Health & personal care). The results showed that the ELMo model outperformed the six standard baselines, including the fine-tuned Bidirectional Encoder Representations from Transformers (BERT) model. In addition, ELMo achieved Mean Square Error (MSE) of 0.0887 and 0.0786 respectively on two datasets and MSE of 0.0791 and 0.0708 with the wrapper method. This results in the reduction of 1.43% and 1.63% in MSE as compared to the fine-tuned BERT model on respective datasets. However, the LDA model has a comparable performance with the fine-tuned BERT model but outperforms the other five baselines. The proposed framework demonstrated good generalization abilities by uncovering important factors of product reviews and can be evaluated on other voting platforms.","PeriodicalId":50355,"journal":{"name":"Intelligent Data Analysis","volume":"9 2","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139268452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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