The Information Fusion Technology of the Attitude Angle Based on the BP Neural Network

Lin, Feng; Fan, Sha Sha; Qu, Xiao Guang

doi:10.4028/www.scientific.net/amm.577.382

Cited by 1 publication

(1 citation statement)

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“…The training dataset will be𝐷 to train the meta-classifier. Then the test dataset is utilized 𝐷 and the trained metaclassifier, the test set is predicted to get the final classification result [6] .In Python's machine learning module, each classifier calculates its probability for each category of the sample, and then those categories with the highest probability are used as the output.The flowchart of the Stacking algorithm is shown in Fig. 1.…”

Section: Stacking Integrated Learning Approachmentioning

confidence: 99%

Study of Bursary Grade Prediction Based on Integrated Learning

Niu,

2023

FBEM

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With the advancement of digital campus, many scholars have addressed the problems of low efficiency of financial aid work in colleges and universities and insufficient fairness and impartiality of the evaluation process, and proposed the solution of using big data and machine learning algorithms to identify the students who are really in need of help and realise the fairness of bursary awarding. Firstly, multi-dimensional feature vectors are constructed by processing and analysing students' daily behavioural data, then Multi-SMOTE oversampling technology is used to solve the problem of balancing classification samples in the process of bursary evaluation, and then Stacking model fusion algorithms are used to integrate multiple classifiers in order to improve the prediction accuracy. The experimental results prove that the method is significantly better than a single classification model in terms of precision and recall, which can improve the efficiency of college financial aid work, as well as ensure the fairness and accuracy of the financial aid process and provide better financial aid services for students.

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Section: Stacking Integrated Learning Approachmentioning

confidence: 99%