Maximum Information Gain Relief Feature Weighting

Zhang, Qian; Jia, Jun; Ding, Jian; Kuang, Haopeng; Chen, Zehan

doi:10.1145/3349341.3349481

Search citation statements

Order By: Relevance

Paper Sections

Select...

A Model For Predicting Landslide Displacement1

Introduction1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

Publication Types

Select...

Other1

Article1

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

(2 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The maximum information coefficient [10][11][12] is a measure used to measure the degree of correlation between two variables x and y. This coefficient has two important attributes: universality and uniformity.…”

Section: A Model For Predicting Landslide Displacementmentioning

confidence: 99%

Research on big data and artificial intelligence technology in geological disaster risk monitoring and early warning

Xu,

2023

International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023)

View full text Add to dashboard Cite

Section: A Model For Predicting Landslide Displacementmentioning

confidence: 99%

Research on big data and artificial intelligence technology in geological disaster risk monitoring and early warning

Xu,

2023

International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023)

View full text Add to dashboard Cite

“…The method can be combined with any training methods, and has high flexibility and high operation efficiency. Therefore, the study selects to use Relief feature selection of Filter Method [22,29].…”

Section: Introductionmentioning

confidence: 99%

Relief Feature Selection and Parameter Optimization for Support Vector Machine based on Mixed Kernel Function

Zhang¹

2018

IJPE

View full text Add to dashboard Cite

In order to improve the classification performance of Support Vector Machine (SVM), Relief feature selection algorithm was used to obtain the most relevant feature subset and remove redundant features. The mixed kernel function, which combined the global kernel function with the local kernel function, was proposed to strengthen the learning ability and generalization performance of SVM. In addition, the parameter optimization of SVM, which combined Genetic Algorithm (GA) with grid search, was performed to reduce computation time and find optimal solutions. Finally, the methods presented in this paper were used in the Heart disease data set and the Breast cancer data set in the UCI. Compared with KNN and BP neural network, the classification result of SVM model with Relief algorithm and mixed kernel function significantly outperformed the other comparable classification model and the experimental results demonstrate the validity of the proposed model.

show abstract

Maximum Information Gain Relief Feature Weighting

Cited by 2 publications

References 4 publications

Research on big data and artificial intelligence technology in geological disaster risk monitoring and early warning

Research on big data and artificial intelligence technology in geological disaster risk monitoring and early warning

Relief Feature Selection and Parameter Optimization for Support Vector Machine based on Mixed Kernel Function

Contact Info

Product

Resources

About