Advances in computational intelligence最新文献_第8页

The sparse factorization of nonnegative matrix in distributed network 分布式网络中非负矩阵的稀疏因子分解

Advances in computational intelligence Pub Date : 2021-09-11 DOI: 10.1007/s43674-021-00009-5

Xinhong Meng, Fusheng Xu, Hailiang Ye, Feilong Cao

引用次数: 0

Semi-supervised multi-label feature selection with local logic information preserved 保留局部逻辑信息的半监督多标签特征选择

Advances in computational intelligence Pub Date : 2021-09-06 DOI: 10.1007/s43674-021-00008-6

Yao Zhang, Yingcang Ma, Xiaofei Yang, Hengdong Zhu, Ting Yang

{"title":"Semi-supervised multi-label feature selection with local logic information preserved","authors":"Yao Zhang, Yingcang Ma, Xiaofei Yang, Hengdong Zhu, Ting Yang","doi":"10.1007/s43674-021-00008-6","DOIUrl":"10.1007/s43674-021-00008-6","url":null,"abstract":"<div><p>In reality, like single-label data, multi-label data sets have the problem that only some have labels. This is an excellent challenge for multi-label feature selection. This paper combines the logistic regression model with graph regularization and sparse regularization to form a joint framework (SMLFS) for semi-supervised multi-label feature selection. First of all, the regularization of the feature graph is used to explore the geometry structure of the feature, to obtain a better regression coefficient matrix, which reflects the importance of the feature. Second, the label graph regularization is used to extract the available label information, and constrain the regression coefficient matrix, so that the regression coefficient matrix can better fit the label information. Third, the <span>(L_{2,p})</span>-norm <span>(0<ple 1)</span> constraint is used to ensure the sparsity of the regression coefficient matrix so that it is more convenient to distinguish the importance of features. In addition, an iterative updating algorithm with convergence is designed and proved to solve the above problems. Finally, the proposed method is validated on eight classic multi-label data sets, and the experimental results show the effectiveness of the proposed algorithm.</p></div>","PeriodicalId":72089,"journal":{"name":"Advances in computational intelligence","volume":"1 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43674-021-00008-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50457474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Generative adversarial networks for open information extraction 用于开放信息提取的生成对抗性网络

Advances in computational intelligence Pub Date : 2021-08-02 DOI: 10.1007/s43674-021-00006-8

Jiabao Han, Hongzhi Wang

{"title":"Generative adversarial networks for open information extraction","authors":"Jiabao Han, Hongzhi Wang","doi":"10.1007/s43674-021-00006-8","DOIUrl":"10.1007/s43674-021-00006-8","url":null,"abstract":"<div><p>Open information extraction (Open IE) is a core task of natural language processing (NLP). Even many efforts have been made in this area, and there are still many problems that need to be tackled. Conventional Open IE approaches use a set of handcrafted patterns to extract relational tuples from the corpus. Secondly, many NLP tools are employed in their procedure; therefore, they face error propagation. To address these problems and inspired by the recent success of Generative Adversarial Networks (GANs), we employ an adversarial training architecture and name it Adversarial-OIE. In Adversarial-OIE, the training of the Open IE model is assisted by a discriminator, which is a (Convolutional Neural Network) CNN model. The goal of the discriminator is to differentiate the extraction result generated by the Open IE model from the training data. The goal of the Open IE model is to produce high-quality triples to cheat the discriminator. A policy gradient method is leveraged to co-train the Open IE model and the discriminator. In particular, due to insufficient training, the discriminator usually leads to the instability of GAN training. We use the distant supervision method to generate training data for the Adversarial-OIE model to solve this problem. To demonstrate our approach, an empirical study on two large benchmark dataset shows that our approach significantly outperforms many existing baselines.</p></div>","PeriodicalId":72089,"journal":{"name":"Advances in computational intelligence","volume":"1 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s43674-021-00006-8","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50439017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Prediction of the academic performance of slow learners using efficient machine learning algorithm 使用高效机器学习算法预测慢速学习者的学习成绩

Advances in computational intelligence Pub Date : 2021-07-03 DOI: 10.1007/s43674-021-00005-9

R. Geetha, T. Padmavathy, R. Anitha

{"title":"Prediction of the academic performance of slow learners using efficient machine learning algorithm","authors":"R. Geetha, T. Padmavathy, R. Anitha","doi":"10.1007/s43674-021-00005-9","DOIUrl":"10.1007/s43674-021-00005-9","url":null,"abstract":"<div><p>Maintaining of immense measure of data has always been a great concern. With expansion in awareness towards educational data, the amount of data in the educational institutes is additionally expanded. To deal with increasing growth of data leads to the usage of a new approach of machine learning. Predicting student’s performance before the final examination can help management, faculty, as well as students to make timely decisions and avoid failing of students. In addition to this, the usage of sentimental analysis can gain insight to improve their performance on the student’s next term. We have used various machine learning techniques such as XGboost, K-Nearest Neighbor (K-NN), and Support Vector Machine (SVM) to build predictive models. We have evaluated the performance of these techniques in terms of the performance indicators such as accuracy, precision and recall to determine the better technique that gives accurate results. The evaluation shows that XGBoost is superior in the prediction of poor academic performers than SVM and K-NN with large dataset.</p></div>","PeriodicalId":72089,"journal":{"name":"Advances in computational intelligence","volume":"1 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s43674-021-00005-9","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50445567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

On Boolean posets of numerical events 关于数值事件的布尔偏序集

Advances in computational intelligence Pub Date : 2021-06-07 DOI: 10.1007/s43674-021-00004-w

Dietmar Dorninger, Helmut Länger

{"title":"On Boolean posets of numerical events","authors":"Dietmar Dorninger, Helmut Länger","doi":"10.1007/s43674-021-00004-w","DOIUrl":"10.1007/s43674-021-00004-w","url":null,"abstract":"<div><p>With many physical processes in which quantum mechanical phenomena can occur, it is essential to take into account a decision mechanism based on measurement data. This can be achieved by means of so-called numerical events, which are specified as follows: Let <i>S</i> be a set of states of a physical system and <i>p</i>(<i>s</i>) the probability of the occurrence of an event when the system is in state <span>(sin S)</span>. A function <span>(p:Srightarrow [0,1])</span> is called a numerical event or alternatively, an <i>S</i>-probability. If a set <i>P</i> of <i>S</i>-probabilities is ordered by the order of real functions, it becomes a poset which can be considered as a quantum logic. In case the logic <i>P</i> is a Boolean algebra, this will indicate that the underlying physical system is a classical one. The goal of this paper is to study sets of <i>S</i>-probabilities which are not far from being Boolean algebras by means of the addition and comparison of functions that occur in these sets. In particular, certain classes of so-called Boolean posets of <i>S</i>-probabilities are characterized and related to each other and descriptions based on sets of states are derived.</p></div>","PeriodicalId":72089,"journal":{"name":"Advances in computational intelligence","volume":"1 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s43674-021-00004-w","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39645429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Solution of Fractional Optimal Control Problems by using orthogonal collocation and Multi-objective Optimization Stochastic Fractal Search 用正交配置和多目标优化随机分形搜索求解分数最优控制问题

Advances in computational intelligence Pub Date : 2021-06-07 DOI: 10.1007/s43674-021-00003-x

J. V. C. F. Lima, F. S. Lobato, V. Steffen Jr

引用次数: 1

Collaborator recommendation integrating author’s cooperation strength and research interests on attributed graph 基于作者合作实力和研究兴趣的属性图合作者推荐

Advances in computational intelligence Pub Date : 2021-05-31 DOI: 10.1007/s43674-021-00002-y

Donglin Hu, Huifang Ma

{"title":"Collaborator recommendation integrating author’s cooperation strength and research interests on attributed graph","authors":"Donglin Hu, Huifang Ma","doi":"10.1007/s43674-021-00002-y","DOIUrl":"10.1007/s43674-021-00002-y","url":null,"abstract":"<div><p>Collaborator recommendation aims to seek suitable collaborators for a given author. In this paper, we model all authors and their features as an attributed graph, and then perform community search on the attributed graph to locate the best collaborator community. From the early collaborative filtering-based methods to the recent deep learning-based methods, most existing works usually unilaterally weigh the network structure or node attributes, or directly search the community via the given node. We argue that the inherent disadvantage of these methods is that the quality of the node to be recommended may not be high, which can lead to suboptimal recommendation results.</p><p>In this work, we develop a new recommendation framework, i.e., Collaborator Recommendation Integrating Author’s Cooperation Strength and Research Interests (CRISI) on an attributed graph. It improves the quality of recommended node via double-weighting the structure and attributes as well as adopting the node replacement method. This can effectively recommend collaborators who have a close cooperative relationship with the recommended node. We conduct extensive experiments on two real-world datasets, and further analysis shows that the performance of our proposed CRISI model is superior to existing methods.</p></div>","PeriodicalId":72089,"journal":{"name":"Advances in computational intelligence","volume":"1 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s43674-021-00002-y","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50528121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Personalized recommendation: an enhanced hybrid collaborative filtering 个性化推荐：一种增强的混合协同过滤

Advances in computational intelligence Pub Date : 2021-05-22 DOI: 10.1007/s43674-021-00001-z

Parivash Pirasteh, Mohamed-Rafik Bouguelia, K. C. Santosh

引用次数: 8

Correction to: Advances in Computational Intelligence 更正:计算智能的进展

Advances in computational intelligence Pub Date : 2021-01-01 DOI: 10.1007/978-3-030-85099-9_36

I. Rojas, G. Joya, Andreu Català

引用次数: 0

Advances in Computational Intelligence: 16th International Work-Conference on Artificial Neural Networks, IWANN 2021, Virtual Event, June 16–18, 2021, Proceedings, Part II 计算智能的进展:第16届国际人工神经网络工作会议，IWANN 2021，虚拟事件，2021年6月16-18日，会议录，第二部分

Advances in computational intelligence Pub Date : 2021-01-01 DOI: 10.1007/978-3-030-85099-9

引用次数: 0