Prediction and Analysis of Network Literature Value Based on Ant Colony Algorithm

Zhao, Na

doi:10.1007/978-981-99-2287-1_54

Cited by 1 publication

(1 citation statement)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ye et al [41] proposed two innovative wrapper feature selection methods by integrating the ant colony optimization algorithm and hybrid rice optimization. Zhao [42] proposed a value prediction and analysis method of network documents based on ant colony algorithm. Aghelpour et al [43] coupled adaptive neuro-fuzzy inference system with ant colony optimization algorithm to realize the 1-, 2-, and 3-days ahead forecasting of daily streamflow.…”

Section: Introductionmentioning

confidence: 99%

Combination prediction method of students’ performance based on ant colony algorithm

Xu,

Kim

2024

PLoS ONE

View full text Add to dashboard Cite

Students’ performance is an important factor for the evaluation of teaching quality in colleges. The prediction and analysis of students’ performance can guide students’ learning in time. Aiming at the low accuracy problem of single model in students’ performance prediction, a combination prediction method is put forward based on ant colony algorithm. First, considering the characteristics of students’ learning behavior and the characteristics of the models, decision tree (DT), support vector regression (SVR) and BP neural network (BP) are selected to establish three prediction models. Then, an ant colony algorithm (ACO) is proposed to calculate the weight of each model of the combination prediction model. The combination prediction method was compared with the single Machine learning (ML) models and other methods in terms of accuracy and running time. The combination prediction model with mean square error (MSE) of 0.0089 has higher performance than DT with MSE of 0.0326, SVR with MSE of 0.0229 and BP with MSE of 0.0148. To investigate the efficacy of the combination prediction model, other prediction models are used for a comparative study. The combination prediction model with MSE of 0.0089 has higher performance than GS-XGBoost with MSE of 0.0131, PSO-SVR with MSE of 0.0117 and IDA-SVR with MSE of 0.0092. Meanwhile, the running speed of the combination prediction model is also faster than the above three methods.

show abstract

Section: Introductionmentioning

confidence: 99%