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Considering the unique users of scholars' social networks, this paper proposed a collaborative association model of paper and project data based on scholars' social networks. Firstly, the proposed model used the two-step feature selection method to preprocess the data, removed the irrelevant and redundant features and obtained the effective features that affected the association between the paper and the project. Then it would adopt TVSM to calculate the text similarity between the paper and the project, then formed recommendation sets for different papers/projects. Through the social network (SCHOLAT) data for researchers, the model was implemented and applied to SCHOLAT. The online application situation and user feedback show that the model has good accuracy and practicability. Furthermore, it can more fully explore the potential relationship between the paper and the project, provide users with better academic research management services, and propose a novel research method for analyzing the academic big data. [ABSTRACT FROM AUTHOR]

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Feature selection is a common dimension reduction approach for processing high-dimensional big data, but it often involves multiple conflicting feature subsets evaluation objectives which are difficult to balance . To reach a compromise among various feature subset evaluations in feature selection and optimize the performance of subset, this paper proposed a subset evaluation multi-objective optimization based feature selection framework and focused on the application of multi-objective particle swarm optimization( MOPSO) in feature subset evaluation. The framework used sparsity, classification ability and information loss to design multi-objective optimization functions. Then it optimized the weight vectors of the features based on multi-objective optimization algorithm, and selected the " knee" of Pareto solution set as optimal vector. Finally, the framework realized feature selection based on weight vector ranking. This paper designed experiments to compare the performance of MOPSO based feature selection( FS_MOPSO) with four classical methods. The results on several standard data sets show that, FS_MOPSO shows higher classification accuracy in low dimensional space while ensuring less information loss. [ABSTRACT FROM AUTHOR]

Copyright of Journal of East China University of Science & Technology is the property of Journal of East China University of Science & Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Feature selection technology plays an important role in big data analysis, image processing, bioinformatics and other fields . In practical applications, the objectives of reducing the classification error rate and reducing the number of extracted features for facilitating the use of subsequent data, are often two conflicting goals. The multi-object particle swarm optimization based on crowding, mutation, dominance for feature selection ( CMDPSOFS) was a kind of bi-objective optimization algorithm with the minimal number of features and classification error rate in feature-oriented selection applications. The algorithm used three different mutation mechanisms for maintaining swarm diversity and balancing global and local search capabilities. However, the uniform variation increased the randomness of the algorithm, resulting in the generation of worse solutions, which reduced the convergence speed of the algorithm. This paper proposed an improved CMDPSOFS- II algorithm to introduce the mutation and selection operations of differential evolution algorithm into the CMDPSOFS algorithm. The experimental results show that the CMDPSOFS- II algorithm is superior to the original method in feature selection and better balances global and local search capabilities. [ABSTRACT FROM AUTHOR]

This paper proposed a novel feature selection method based on genetic algorithm for big data processing. Firstly, this method evaluated the features of each dimension, adjusted its weight according to the difference of each feature on the similar nearest neighbor and the heterogeneous nearest neighbor, and guided the search of genetic algorithm based on the feature weight, thus improved the search ability of the algorithm and the accuracy of feature acquisition. And then it combined the feature weights to calculate the fitness of the feature, took fitness as the evaluation index, and started the genetic algorithm to obtain the optimal feature subset, finally achieved an efficient and accurate big data feature selection. The results of experiment show that this method can effectively reduce the number of classification features and improve the accuracy of feature classification. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the existing Internet of Things big data feature selection algorithm has low computational efficiency and low scalability, this paper proposed a system architecture that selected features by using improved artificial bee colony. The architecture included a four-layer system and it could efficiently aggregate the effective data and eliminated unwanted data. The entire system was based on the Hadoop platform, MapReduce, and improved ABC algorithm. The method used improved ABC algorithm to select features and it also used a parallel algorithm to support MapReduce, which could efficiently process a huge volume of data sets. It used MapReduce tool to implement the system and used particle filter for removal of noise. The proposed algorithm and similar algorithms were evaluated for the efficiency, accuracy and throughput by using ten different data sets. The results show that the proposed algorithm is more scalable and efficient in selecting features. [ABSTRACT FROM AUTHOR]

With the development of high performance computer and data sharing platform, a large number of high fidelity turbulence data can be obtained. Recently, due to the evolution of artificial intelligence, like deep neural network, data-driven machine learning methods have been adopted to quantify the model uncertainty and improve and construct turbulence models. The combination of big turbulence data and artificial intelligence becomes a new area of turbulence research. Although some encouraging results have been achieved, there are still many difficulties and challenges, such as the generalization ability and robustness of the models, etc. The modeling process involves various aspects including data process, feature selection and selection and optimization of the model framework, etc. This paper analyzes and summarizes the main research progress from two aspects:the implementation methods of machine learning in turbulence modeling and the different model targets. Besides, the challenges and future works in this area are also discussed. [ABSTRACT FROM AUTHOR]

The existing filler feature selection algorithms do not consider the inner structure of nonlinear data, lead to a lower classification accuracy than wrapper feature selection methods. This paper proposed a reproducing kernel Hilbert space mapping based feature selection algorithm to solve that shortcoming of filter feature selection algorithms. Firstly, it constructed the search tree based on branch and bound method and searched. Then, based on the reproducing kernel Hilbert space mapping, it analyzed the inner structure of nonlinear data. Lastly, based on the inner structure of the data, it selected the optimal distance computing method. Compared simulation experiments results show that the proposal has a similar classification accuracy with wrapper feature selection algorithms, at the same time has obviously heller computational efficiency, and can handle the big data analysis. [ABSTRACT FROM AUTHOR]

In order to improve the efficiency of data statistics and analysis, it is necessary to cluster data sets, for reduces the data sets collection dimension and removes similar data redundancy. This paper used entropy weighted and sparse fractional feature selection. On the one hand, it divided the local structure of heterogeneous data, reduced the data dimension, marked and sorted the feature importance of local structure, and improved the clustering accuracy. Experimental results show that the method has strong applicability to different kinds of large data clustering. [ABSTRACT FROM AUTHOR]