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Few-Shot Partial Multi-View Learning

IEEE Transactions on Pattern Analysis and Machine Intelligence 2023年第10期45卷 11824-11841页

作者： Zhou, Yuan Guo, Yanrong Hao, Shijie Hong, Richang Luo, Jiebo Ministry of Education Hefei University of Technology The Key Laboratory of Knowledge Engineering with Big Data Hefei230009 China Hefei University of Technology The School of Computer Science and Information Engineering Hefei230009 China University of Rochester The Department of Computer Science RochesterNY14627 United States

It is often the case that data are with multiple views in real-world applications. Fully exploring the information of each view is significant for making data more representative. However, due to various limitations and failures in data collection and pre-processing, it is inevitable for real data to suffer from view missing and data scarcity. The coexistence of these two issues makes it more challenging to achieve the pattern classification task. Currently, to our best knowledge, few appropriate methods can well-handle these two issues simultaneously. Aiming to draw more attention from the community to this challenge, we propose a new task in this paper, called few-shot partial multi-view learning, which focuses on overcoming the negative impact of the view-missing issue in the low-data regime. The challenges of this task are twofold: (i) it is difficult to overcome the impact of data scarcity under the interference of missing views;(ii) the limited number of data exacerbates information scarcity, thus making it harder to address the view-missing issue in turn. To address these challenges, we propose a new unified Gaussian dense-anchoring method. The unified dense anchors are learned for the limited partial multi-view data, thereby anchoring them into a unified dense representation space where the influence of data scarcity and view missing can be alleviated. We conduct extensive experiments to evaluate our method. The results on Cub-googlenet-doc2vec, Handwritten, Caltech102, Scene15, Animal, ORL, tieredImagenet, and Birds-200-2011 datasets validate its effectiveness. The codes will be released at https://***/zhouyuan888888/UGDA. © 1979-2012 IEEE.

关键词： Neural networks

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Sentimental analysis of web financial reviews opportunities and challenges 6

Sentimental analysis of web financial reviews opportunities ...

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6th International Conference on knowledge Discovery and Information Retrieval, KDIR 2014

作者： Wan, Changxuan Jiang, Tengjiao Liu, Dexi Liao, Guoqiong School of Information and Technology Jiangxi University of Finance and Economics Nanchang China Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics Nanchang China

ISBN: (纸本)9789897580482

Web financial reviews are real-time, comprehensive and authentic. The construction and quantification of Web financial indexes based on Web financial reviews is of great significance for the financial early warning for enterprises. Comparing with product reviews and news commentaries, in Web financial reviews, the opinion targets have more diverse compositions, the frequencies of opinion targets' occurrence vary greatly, and the sentiment words' have more diverse parts of speech. These characteristics make the extraction of opinion targets, the construction of Web financial indexes, and opinion targets-based sentimental analysis all more complicated, posing new challenges to natural language processing. Copyright © 2014 SCITEPRESS - Science and Technology Publications All rights reserved.

关键词： Sentiment analysis

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Research on XML element search results clustering

Research on XML element search results clustering

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6th International Conference on Management of e-Commerce and e-Government, ICMeCG 2012

作者： Zhong, Min-Juan Wan, Chang-Xuan Liu, De-Xi Jiao, Xian-Pei School of Information Technology Jiangxi University of Finance and Economics Nanchang China Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics Nanchang China

ISBN: (纸本)9781467329439

Clustering XML search results is an effective way to improve performance. However, the key problem is how to measure similarity between XML documents. This paper studies XML search results clustering based on element granularity and proposes one similarity measurement method. The method firstly uses latent semantic indexing technology(LSI) to obtain term semantics and then combines the XML element node content and semantic structure properties(CASS). To evaluate clustering performance, two new performance evaluation methodologies, namely R-ClusterRatio and R-DocuRatio are introduced. It is motivated by the observations of relevant documents distribution and the fact that the experiment data collection, IEEE CS corpus, do not provide classification information. Experiment results show that proposed similarity method combining term semantics with content and structure semantics integration(LSI-CASS) is feasible, and it produces better clustering quality than LSI-CAS. © 2012 IEEE.

关键词： Semantics

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Clustering XML search results based on content and structure similarity

Clustering XML search results based on content and structure...

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2011 International Conference on Management of e-Commerce and e-Government, ICMeCG 2011

作者： Min-Juan, Zhong Chang-Xuan, Wan De-Xi, Liu Xian-Pei, Jiao School of Information and Technology Jiangxi University of Finance and Economics Nanchang China Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics Nanchang China

ISBN: (纸本)9780769545448

Clustering XML search results is an effective way to improve performance. However, the key problem is how to measure similarity between XML documents. In this paper, we propose a semantic similarity measure method combining content with structure, in which a variety of XML document features, including term element frequency, term inverse element frequency, semantic weight of tag label and level information of the term, are analyzed and applied for computing the similarity between XML documents. In addition, two new performance evaluation methodology, namely Cluster Ratio Relevant and Docu Ratio Relevant, for clustering quality are introduced motivated by the observations of relevant documents distribution and the fact that collection has no classification information. Experiment results show that proposed similarity method(CAS measure)outperforms traditional document clustering(CO measure) in Cluster Ratio Relevant and Docu Ratio Relevant and produces better clustering quality. © 2011 IEEE.

关键词： XML

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A novel hybrid butterfly optimization algorithm for feature selection with sine cosine velocity in the high-dimensional classification data

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Journal of Intelligent and Fuzzy Systems 2024年第5-6期47卷 369-391页

作者： Zhang, Li Chen, Xiaobo Key Laboratory of Data Science and Intelligence Education Hainan Normal University Ministry of Education Haikou Hainan China School of Computer Engineering Jiangsu University of Technology Changzhou Jiangsu China Changzhou City Center Branch People's Bank of China Changzhou Jiangsu China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun China

Aiming at the shortcomings of the traditional butterfly optimization algorithm in solving the high-dimensional classification feature selection problem, which has low convergence and is prone to fall into local optimal solutions, a new hybrid butterfly optimization algorithm is proposed, i.e., HBOA-SCV (A novel hybrid butterfly optimization algorithm with sine cosine velocity). The algorithm is applied to solve a high-dimensional classification feature selection problem. Firstly, the algorithm's global exploration and local exploitation ability can be dynamically balanced by introducing inertia weight coefficients w based on multiple learning strategies. Secondly, using the updated speed position formula of the sine-cosine acceleration strategy, individual butterflies' autonomous search ability and convergence speed can be further improved. Finally, according to the fitness value of each butterfly individual, the moving step length and direction of the butterfly individual are automatically adjusted better to fit the actual search process of the butterfly individual, increase the search ability in the global range, and avoid the algorithm from falling into the local optimum. To verify the algorithm's effectiveness, 18 high-dimensional classification numbers are selected to carry out simulation and comparison experiments between HBOA-SCV and traditional BOA algorithm, five improved BOA algorithms and other comparative algorithms for high-dimensional classification data successively. The experimental results show that the average fitness value and classification accuracy of the HBOA-SCV algorithm are better than the comparison algorithm, thus verifying the superiority of the HBOA-SCV algorithm. © 2024 - IOS Press. All rights reserved.

关键词： Optimization algorithms

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Fuzzy Rough Based Decision Tree Constructing

Fuzzy Rough Based Decision Tree Constructing

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第十二届中国Rough集与软计算学术会议、第六届中国Web智能学术研讨会及第六届中国粒计算学术研讨会联合学术会议

作者： CHEN Wei ZHAO Su-yun CHEN Hong LI Cui-ping Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of ChinaMOE Beijin Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of ChinaMOE Beijin

Decision tree and fuzzy rough set are two distinct but complementary *** tree is a simple and easy-understandable rule-based classifier,whereas the tool of fuzzy rough sets are effective on attribute and sample *** is promising to propose an approach to integrate these two rule based classification tools to construct a novel decision tree based on fuzzy rough *** this paper,based on the basic concept of fuzzy rough sets,i.e.,consistence degree,we propose a fuzzy rough decision tree which is completely different from the existing classification *** three key basic elements of decision tree,i.e.,node,branch and leaf,are designed in a new way by using the notions in fuzzy rough *** then one algorithm to build fuzzy rough tree classifier is ***,experimental results show that the proposed algorithm is readily comprehensible and effective.

关键词： Fuzzy rough sets Decision tree Consistence degree Rule-based classifier

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Swot analysis of Chinese rural information society policies

Swot analysis of Chinese rural information society policies

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2nd International Conference on Mechatronics and Industrial Informatics, ICMII 2014

作者： Zhou, Ping School of Information Technology Jiangxi University of Finance and Economics Nanchang China Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics Nanchang China

ISBN: (纸本)9783038351764

This paper presents an analysis of strengths, weaknesses, opportunities, and threats (SWOT) for Chinese rural information society development (CRISD) policies in terms of its missions and strategies. After briefly describing the institutional and information society development contexts, the paper draws on literature reviews and questionnaires survey to list of SWOT factors for CRISD. These findings could be used to help adjust the next step work to support decision making for rural information society construction and to provide a valuable reference for other investors who are planning to invest in information society programs in China's rural regions. This article presents valuable missions and strategic goals through the SWOT analysis for realizing information society development in Chinese rural areas. © (2014) Trans Tech Publications, Switzerland.

关键词： Rural areas

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An effective XML identifying feedback documents method based on two-stage ranking model for pseudo-relevance feedback

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2013 3rd International Symposium on Chemical engineering and Material Properties, ISCEMP 2013

作者： Zhong, Min Juan School of Information Technology Jiangxi University of Finance and Economics Nanchang China JiangXi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics Nanchang China

ISBN: (纸本)9783037858363

Although pseudo relevant feedback is an effective query expansion method, query drift away from the topic has been occurred frequently. Therefore, the first important problem is how to identify relevant documents in the top retrieved set and form the good feedback source. In this paper, an effective XML identifying feedback documents method is proposed, in which a two-stage ranking model is presented and the relevant XML documents are found. The experiment results show that the proposed method is reasonable and the quality of feedback source is ensured. © (2013) Trans Tech Publications, Switzerland.

关键词： XML

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Finding top-k min-cost connected trees in databases

Finding top-k min-cost connected trees in databases

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23rd International Conference on data engineering, ICDE 2007

作者： Ding, Bolin Yu, Jeffrey Xu Wang, Shan Qin, Lu Zhang, Xiao Lin, Xuemin Chinese University of Hong Kong Hong Kong Key Laboratory of Data Engineering and Knowledge Engineering MOE of China Renmin University of China China University of New South Wales NICTA Australia

ISBN: (纸本)1424408032

It widely realized that the integration of database and information retrieval techniques will provide users with a wide range of high quality services. In this paper, we study processing an l-keyword query, p1, p2, , p1, against a relational database which can be modeled as a weighted graph, G(V, E). Here V is a set of nodes (tuples) and E is a set of edges representing foreign key references between tuples. Let Vi⊆7 V be a set of nodes that contain the keyword pi. We study finding top-k minimum cost connected trees that contain at least one node in every subset Vi, and denote our problem as GST-k When k = 1, it is known as a minimum cost group Steiner tree problem which is NP-Complete. We observe that the number of keywords, l, is small, and propose a novel parameterized solution, with l as a parameter, to find the optimal GST-1, in time complexity O(3ln + 2l ((l + log n)n + m)), where n and m are the numbers of nodes and edges in graph G. Our solution can handle graphs with a large number of nodes. Our GST-1 solution can be easily extended to support GST-k, which outperforms the existing GST-k solutions over both weighted undirected/directed graphs. We conducted extensive experimental studies, and report our finding. © 2007 IEEE.

关键词： Trees (mathematics)

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DPListCF: A Differentially Private Approach for Listwise Collaborative Filtering

DPListCF: A Differentially Private Approach for Listwise Col...

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IEEE Symposium on Computers and Communications

作者： Yuncheng Wu Juru Zeng Hong Chen Yao Wu Wenjuan Liang Hui Peng Cuiping Li Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education China

ISBN: (纸本)9781509006809

Recently, listwise ranking-oriented collaborative filtering (CF) algorithms have gained great success in recommender systems. However, the ranked preference list may compromise the privacy of individuals. A notable paradigm for offering strong privacy guarantee is differential privacy. In this paper, we propose DPListCF, a differentially private algorithm based on ListCF (a state-of-art listwise CF algorithm). The main idea of DPListCF is to make both of the similarity calculation phase and rank prediction phase of ListCF satisfy differential privacy, by using input perturbation method and output perturbation method in the two phases respectively. Extensive experiments using two real datasets evaluate the performance of DPListCF, and demonstrate that the proposed algorithm outperforms state-of-art approaches.

关键词： Open area test sites Privacy collaborative filtering Recommender systems Perturbation methods Arts CONTAINMENT FAILURE

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