Zhaolong Gao

De La Salle University – Dasmariñas

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Mengvi P. Gatpandan

De La Salle University – Dasmariñas

Paulino H. Gatpandan

De La Salle University – Dasmariñas

Jiajia Xu

South China University of Technology

Cooperative Institutions

De La Salle University – Dasmariñas

AMA Computer University

José Rizal University

South China University of Technology

Zhongda Hospital Southeast University

Shangrao Normal University

Fujian Jiangxia University

Zhejiang University

Stomatology Hospital

Anhui Sanlian University

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Freight and Passenger Volume Prediction using Multiple Regression

2022 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) (2021)

Jiajia Xu Paulino H. Gatpandan Mengvi P. Gatpandan Zhaolong Gao

This paper analyzes the influencing factors of highway passenger and freight traffic, determines its influencing factors, and collects relevant statistical data from 117 different regions. Based on the principle of multiple linear regression method, first all variables are incorporated into the multiple regression equation for simulation. Second, integrate, demonstrate the applicability of the model, and then use the stepwise multiple regression method for model fitting. Based on this idea, the multiple linear regression model is constructed and forecasted for the highway passenger and freight volume. The results show that the stepwise multiple regression is effective. While the number of variables is greatly reduced and the calculation process is simplified, the model's fit is still good, and the problem of collinear between multiple variables is solved, and the regression coefficient of the variable is not consistent with the actual problem, and the result is predicted, It is also consistent with the actual situation and the applicability of the verification method, which can provide application references for road passenger and freight volume forecasting in other related areas.

Stepwise regression

Variables

Regression diagnostic

10.1109/mlise54096.2021.00022

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Classification Decision Tree Algorithm in Predicting Students’ Course Preference

Zhaolong Gao Mengvi P. Gatpandan Paulino H. Gatpandan

With the continuous development of machine learning, more and more applications are applied to all areas of life. Decision tree algorithm, as a classic algorithm in machine learning, is also widely used in various industries. In recent years, the informatization of the college entrance examination has allowed the admissions department to accumulate a large amount of college entrance examination data. Based on the decision tree algorithm in machine learning, the candidate data is analyzed, and an algorithm for predicting candidates to apply for the major is proposed, and the admission data of a certain university in Yantai is used as an experiment. The data set tests the prediction accuracy of the algorithm. Two complete decision trees have been constructed to provide high school seniors with intelligent decisions and suggestions for a series of basic issues such as college selection, major selection, and voluntary reporting.

ID3 algorithm

Informatization

Statistical classification

10.1109/isceic53685.2021.00026

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Citations (4)