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Copyright of Geomatics & Information Science of Wuhan University is the property of Geomatics & Information Science of Wuhan University 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.)

In the big data environment, the random forest algorithm suffers from excessive redundancy and irrelevant features, the insufficient spatial information content of feature subspace, and low parallelization efficiency. To resolve these issues, this paper presented PRFGRSAE. Firstly, this algorithm proposed a DRNGRSAE, which filtered redundant and irrelevant features of the feature set and extracted features by stacked auto-encoders to reduce the number of redundant and irrelevant features effectively. Secondly, it proposed a SSLF that combined Latin hypercube sampling and normalized correlation degree, which formed feature subspaces with high spatial expression by performing multi-layer division sampling on the feature set, and ensured the feature subspace information content. Finally, it proposed a reducer allocation strategy DSVLA combining with variable action learning automata, which allocated each cluster to reducers for processing evenly and improved the parallelization efficiency effectively. Experimental results show that compared with IMRF, KSMRF, and GAPRF algorithms, the speedup ratio and accuracy of the PRFGRSAE algorithm are significantly improved. Therefore, the algorithm can obtain higher accuracy and parallel efficiency when applied to process large data, especially for data sets with more features. [ABSTRACT FROM AUTHOR]

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology 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.)

Copyright of Journal of Agricultural Science & Technology (1008-0864) is the property of Journal of Agricultural Science & Technology 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.)

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]

For research on the efficiency of processing large data sets with K-nearest neighbor(KNN) method, this paper proposed a distributed exact fuzzy-KNN classification algorithm based on Spark framework. The method innovatively combined the Spark framework distributed map and reduce processes with the fuzzy-KNN. Firstly, it processed the training sample category information in different partitions to obtain the class membership degree. It converted the training set into a fuzzy training set with adding membership degrees, and then used the KNN algorithm to calculate the k nearest neighbor of the previously calculated class member test set. Finally, it was classified by distance weight. Experiments on mega-scale dataset samples and comparison experiments with other algorithms show that this method is feasible and effective . [ABSTRACT FROM AUTHOR]

The evaluation of subhealth of traditional college teachers is lack of timeliness, objectivity and efficiency. In the big data environment, the subhealth assessment model can be established more effectively by information technology, so as to support the evaluation and prediction of subhealth state of university teachers. Firstly, according to the analysis of multidimensional subhealth impact factors of university teachers, the paper constructs a decision conceptual model of the impact of multidimensional subhealth, followed by the analysis and data processing of the sample data characteristics, based on the detailed process of using CART algorithm. Finally, the parallel implementation process and experimental verification based on Spark are given. The subhealth concept model of university teachers objectively reflects the evaluation factors of sub health. The decision tree model can support the prediction and analysis of subhealth of university teachers, and experiments verify the validity, immediacy and accuracy of the model. [ABSTRACT FROM AUTHOR]