Open AccessThe Computer Intelligent Selection of Scientific Research Subjects Through Ensemble Learning for Large-Scale Data Sources and Deep Neural Network
Author(s) -
Yan Ma,
Lida Zou,
Ke Liu,
Yingkun Han,
Lei Ma
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2083/3/032094
Subject(s) - computer science , artificial intelligence , machine learning , artificial neural network , selection (genetic algorithm) , support vector machine , decision tree , ensemble learning , random forest , feature selection , big data , recall , data mining , philosophy , linguistics
Selecting a proper scientific research subject is critical for scientific researchers and managers. Scientific researching data are from massive sources and have various attributes. For the problem of subject selection, feature extraction and prediction model play important role in performance optimization. In the paper we introduce ensemble learning method to help find the best fit attributes describing data. Our ensemble learning models include random forests, support vector machine, Boltzmann machine and decision tree. Since the data are from many data sources, we adopt multiple models of deep neural network. An acceleration method is used to reduce the training time as well. Experiments shows that the proposed approach performs better than RNN algorithm both in accuracy ratio and recall ratio. The model selection module and acceleration method help optimize the time cost largely.