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Service recommendation based on quotient space granularity analysis and covering algorithm on Spark

KNOWLEDGE-BASED SYSTEMS 2018年第May 1期147卷 25-35页

作者： Zhang, Yi-wen Zhou, Yuan-yuan Wang, Fu-tian Sun, Zheng He, Qiang Anhui Univ Minist Educ Key Lab Intelligent Comp & Signal Proc Hefei Anhui Peoples R China Arcadia Univ Sch Sci Glenside PA USA Swinburne Univ Technol Sch Software & Elect Engn Melbourne Vic Australia

The rapid growth of Web services has made it a challenge for users to find appropriate Web services because it is very difficult for traditional Web service recommendation approaches to process the large amount of service-relevant data. To address this issue, this paper proposes CA-QGS (covering algorithm based on Quotient space Granularity analysis on Spark), a scalable approach for accurate Web service recommendation in large-scale scenarios. CA-QGS first clusters users and Web services based on users' past quality experiences on co-invoked Web services. It then performs granularity analysis on the clustering results to identify users and Web services that are similar to the target user and Web service, and employs the collaborate filtering technique to predict the quality of the target Web service for the target user. This way, appropriate Web services can finally be recommended to the target user. To increase the efficiency of CA-QGS, we parallelize CA-QGS on Spark. Extensive experiments show that CA-QGS outperforms existing approaches in both recommendation accuracy and efficiency. (c) 2018 Elsevier B.V. All rights reserved.

关键词： covering algorithm Quotient space Recommender systems Association matrices

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On the Generalized covering Radii of Reed-Muller Codes

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IEEE TRANSACTIONS ON INFORMATION THEORY 2022年第7期68卷 4378-4391页

作者： Elimelech, Dor Wei, Hengjia Schwartz, Moshe Ben Gurion Univ Negev Sch Elect & Comp Engn IL-8410501 Beer Sheva Israel Peng Cheng Lab Shenzhen 518000 Peoples R China

We study generalized covering radii, a fundamental property of linear codes that characterizes the trade-off between storage, latency, and access in linear data-query protocols such as PIR. We prove lower and upper bounds on the generalized covering radii of Reed-Muller codes, as well as finding their exact value in certain extreme cases. With the application to linear data-query protocols in mind, we also construct a covering algorithm that gets as input a set of points in space, and find a corresponding set of codewords from the Reed-Muller code that are jointly not farther away from the input than the upper bound on the generalized covering radius of the code. We prove that the algorithm runs in time that is polynomial in the code parameters.

关键词： Codes Reed-Muller codes Linear codes Protocols Upper bound Generators Hamming weight Reed-Muller codes generalized covering radius covering algorithm

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covering-Based Web Service Quality Prediction via Neighborhood-Aware Matrix Factorization

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2021年第5期14卷 1333-1344页

作者： Zhang, Yiwen Wang, Kaibin He, Qiang Chen, Feifei Deng, Shuiguang Zheng, Zibin Yang, Yun Anhui Univ Sch Comp Sci & Technol Hefei Anhui Peoples R China Swinburne Univ Technol Sch Software & Elect Engn Melbourne Vic Australia Deakin Univ Sch Informat Technol Melbourne Vic Australia Zhejiang Univ Coll Comp Sci & Technol Hangzhou Zhejiang Peoples R China Sch Data & Comp Sci Guangzhou Guangdong Sheng Peoples R China

The number of Web services on the Internet has been growing rapidly. This has made it increasingly difficult for users to find the right services from a large number of functionally equivalent candidate services. Inspecting every Web service for their quality value is impractical because it is very resource consuming. Therefore, the problem of quality prediction for Web services has attracted a lot of attention in the past several years, with a focus on the application of the Matrix Factorization (MF) technique. Recently, researchers have started to employ user similarity to improve MF-based prediction methods for Web services. However, none of the existing methods has properly and systematically addressed two of the major issues: 1) retrieving appropriate neighborhood information, i.e., similar users and services;2) utilizing full neighborhood information, i.e., both users' and services' neighborhood information. In this paper, we propose CNMF, a covering-based quality prediction method for Web services via neighborhood-aware matrix factorization. The novelty of CNMF is twofold. First, it employs a covering-based clustering method to find similar users and services, which does not require the number of clusters and cluster centroids to be prespecified. Second, it utilizes neighborhood information on both users and services to improve the prediction accuracy. The results of experiments conducted on a real-world dataset containing 1,974,675 Web service invocation records demonstrate that CNMF significantly outperforms eight existing quality prediction methods, including two state-of-the-art methods that also utilize neighborhood information with MF.

关键词： Web services Quality of service Predictive models Prediction algorithms Computer science Time factors Quality of service quality prediction matrix factorization neighborhood information covering algorithm

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An improved pattern-based prediction model for a class of industrial processes

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2022年第7期44卷 1410-1423页

作者： Wang, Mushu Lu, Yanrong Pan, Weigang Shandong Jiaotong Univ Sch Informat Sci & Elect Engn Jinan 250357 Peoples R China Lanzhou Univ Technol Coll Elect & Informat Engn Lanzhou 730050 Peoples R China

For the problem of simplifying pattern-based modeling procedures, an improved pattern-based modeling method is put forward via pattern classification for a class of complex processes. It is a pure data-driven modeling method using statistical attributes of the processes. At the beginning of the paper, a method of system dynamics description based on pattern moving is introduced. Then, an improved method of pattern-moving-based prediction modeling is put forward, and it simplifies the pattern-moving-based modeling method by integrating an initial model and a classification mapping. It consists of two parts: system pattern construction and pattern classification. And a constructive classification neural network (CCNN) is designed to describe system dynamics by classifying the system pattern, and its generalization is discussed. Finally, simulations using data of an actual production process demonstrate the feasibility of the proposed modeling method, and the effectiveness of the CCNN is verified using comparison experiments.

关键词： Pattern moving covering algorithm constructive neural network pattern classification system modeling complex production process

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A covering method for continuous global optimisation

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INTERNATIONAL JOURNAL OF COMPUTING SCIENCE AND MATHEMATICS 2021年第4期13卷 369-390页

作者： Ziadi, Raouf Becherif-Madani, Abdelatif Univ Ferhat Abbas Setif 1 Lab Fundamental & Numer Math LMFN Dept Math Setif 19000 Algeria

In this paper, we improve the reducing transformation method for solving a large class of global optimisation problems. The reducing transformation method allows us to transform a multidimensional problem into a one-dimensional one of the same type, and then use the one-dimensional Evtushenko algorithm to obtain the global minimum. To accelerate the corresponding mixed algorithm (Reducing transformation-Evtushenko), we have incorporated the Hook-Jeeves algorithm to explore promising regions. Our approach is suitable for solving a large class of global optimisation problems on a rectangle of Double-struck capital R-n where the objective function is only continuous. This method converges in a finite number of iterations to the global minimum within a prescribed accuracy delta > 0. Numerical experiments are achieved on some typical test problems and a comparison with well known methods is carried out to show the performance of our algorithm.

关键词： global optimisation covering algorithm reducing transformation method alpha-dense curves Evtushenko's algorithm Hooke-Jeeves algorithm

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An Improved Classification Model Based on covering algorithm and SVM

An Improved Classification Model Based on Covering Algorithm...

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13th International Conference on Control, Automation and Systems (ICCAS)

作者： Shi, Yang Cho, Young-Im Univ Suwon Coll Informat & Technol Hwaseong 445743 South Korea Qilu Univ Technol Coll Informat Jinan 250353 Peoples R China

ISBN: (纸本)9788993215052

In order to overcome some shortages of SVM, an improved classification model is introduced in this paper. For the first problem about isolated points or noises mixed in training data sets which will cause overfitting problem and decrease the capability of generalization for SVM, we proposed modified covering algorithm to find out the isolated points and deal with it by the definition of covering sample density. As for the second problem, time cost for training SVM on large data sets usually is high;we introduce modified CA as the pre-classification step to reduce the training sample scale, by constructing a series of covers and deleting the isolated points, and then use the centroids of the rest covers as the new training data sets for SVM training. By the experiments on the real world data sets, results show the training time can drop significantly, and the accuracy is very close to Lib-SVM. So, CA-SVM is an efficient classification model.

关键词： Support Vector Machine covering algorithm covering Sample Density Reduced Data Sets

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A covering algorithm Based on Competition

A Covering Algorithm Based on Competition

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International Workshop on Information and Electronics Engineering (IWIEE) / International Conference on Information, Computing and Telecommunications (ICICT)

作者： Wang, Qingsheng Zhang, Yueqin Sun, Xianyang Liang, Bin Taiyuan Univ Technol Coll Comp Sci & Technol Taiyuan 030024 Peoples R China

In covering algorithm, the order of learning samples is random, but experiments show that the learning sequence has significant impact on the network performance. This paper proposes a new algorithm which is covering algorithm based on competition (CAC). In this algorithm, sphere neighborhoods can be adjusted gradually, the ill-suited sphere neighborhoods will be removed and the whole neural network will performs more stable. Finally the algorithm is applied to a widely used database. The experiment results show that it can effectively decrease the number of rejected samples, reduce the number of sphere domains and improve the recognition accuracy. (C) 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Harbin University of Science and Technology

关键词： neural network covering algorithm learning sequence

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Adopting Relational Reinforcement Learning in covering algorithms for Numeric and Noisy Environments

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2016年第3期9卷 572-594页

作者： ElGibreen, Hebah Aksoy, Mehmet Sabih King Saud Univ Coll Comp & Informat Sci Riyadh 11415 Saudi Arabia

covering algorithms (CAs) constitute a type of inductive learning for the discovery of simple rules to predict future activities. Although this approach produces powerful models for datasets with discrete features, its applicability to problems involving noisy or numeric (continuous) features has been neglected. In real-life problems, numeric values are unavoidable, and noise is frequently produced as a result of human error or equipment limitations. Such noise affects the accuracy of prediction models and leads to poor decisions. Therefore, this paper studies the problem of CAs for data with numeric features and introduces a novel non-discretization algorithm called RULESCONT. The proposed algorithm uses relational reinforcement learning (RRL) to resolve the current difficulties when addressing numeric and noisy data. The technical details of the algorithm are thoroughly explained to demonstrate that RULES-CONT contribute upon the RULES family by collecting its own knowledge and intelligently re-uses previous experience. The algorithm overcomes the infinite-space problem posed by numeric features and treats these features similarly to those with discrete values, while incrementally discovering the optimal rules for dynamic environments. It is the first RRL algorithm that intelligently induces rules to address continuous and noisy data without the need for discretization or pruning. To support our claims, RULES-CONT is compared with 7 well-known algorithms applied to 27 datasets with four levels of noise using 10-fold crossvalidation, and the results are analyzed using box plots and the Friedman test. The results show that the use of RRL results in significantly improved noise resistance compared with all other algorithms and reduces the computation time of the algorithm compared with the preceding version, which does not use relational representation.

关键词： covering algorithm RULES Family Continuous Features and Relational Reinforcement Learning

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Pattern Recognition (PR) Based a New Control Approach for a Kind of Complex Processes

Pattern Recognition (PR) Based a New Control Approach for a ...

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Chinese Automation Congress (CAC)

作者： Xu, Zhengguang Wang, Mushu Guo, Lingli Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing Peoples R China

ISBN: (纸本)9781728113128

For a kind of complex production processes, a new control method is proposed based on pattern recognition technology. It avoids structure design or parameter identification of a system model or controller. First of all, the moving trajectory is predicted through the classification of system pattern samples constructed by input-output classes. And then it is approximately described by a straight-line model. According to the model the system input of a class is obtained. At last, experimental simulations demonstrate the validity of the proposed control method and analysis of parameters affecting control performance is presented.

关键词： pattern recognition covering algorithm discrete- time nonlinear systems stochastic approximation algorithm

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PATTERN RECOGNITION BASED DYNAMICS DESCRIPTION OF PRODUCTION PROCESSES IN METRIC SPACES

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ASIAN JOURNAL OF CONTROL 2017年第4期19卷 1595-1607页

作者： Wang, Mushu Xu, Zhengguang Guo, Lingli Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China

A new type of modeling method is put forward based on pattern recognition (PR) technology for some industrial production processes. The proposed method is a pure data-driven modeling method since the model is independent of the controlled plant, and it is based on the measured input and output (I/O) data of the controlled plant in a closed loop. Different from the traditional modeling method, the system dynamics is described by I/O classes, which are obtained from raw I/O data through partitioning of the data space respectively and I/O orders of the model resort to the conditional entropy. The covering algorithm based pattern classification (PC) is used to establish the mapping between input and output of the proposed model in metric spaces. The experimental results illustrate the feasibility of the modeling method.

关键词： Pattern recognition data driven information entropy covering algorithm k-means clustering dynamics description

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