A Feature Selection Method Based on Information Theory and Genetic Algorithm
Author(s):
Article Type:
Research/Original Article (دارای رتبه معتبر)
Abstract:
Purpose
When dealing with high-dimensional datasets, dimensionality reduction is a crucial preprocessing step to achieve high accuracy, efficiency, and scalability in classification problems. This research aims to introduce a feature selection method for high-dimensional datasets by employing dimensionality reduction and genetic algorithms.Method
In this study, an innovative algorithm has been developed to determine the mutual information between features and the target class using a new criterion. In this method, new characteristics are generated through the combination or transformation of the original characteristics. In this manner, the multi-dimensional space is transformed into a new space with fewer dimensions. In addition to considering the new criterion of mutual information, a genetic algorithm has been employed to enhance the speed of the proposed method.Findings
The performance of this method has been evaluated on datasets of varying dimensions, with the number of features ranging from 13 to 60. The proposed method has been evaluated in comparison to similar methods, focusing on classification accuracy. The results have been promising.Conclusion
The proposed method has been applied using MRMR, DISR, JMI, and NJMIM methods on various datasets. The average accuracies obtained from the proposed method are 65.32%, 74.51%, 70.88%, and 58.2%, indicating the efficiency of the proposed method. According to the results obtained, the proposed method outperformed DISR, JMI, NJMIM, and MRMR on average, except for the sonar data set, where the sonar data set yielded better results than the proposed method.Keywords:
Language:
Persian
Published:
Journal of Sciences and Techniques of Information Management, Volume:9 Issue: 3, 2023
Pages:
7 to 32
https://www.magiran.com/p2672864
سامانه نویسندگان
مقالات دیگری از این نویسنده (گان)
-
review community detection algorithms in multilayer networks;Traditional methods and deep learning
Roozbahani Zahra *,
Engineering Management and Soft Computing, -
User Interface for Scientific Social Networks to Improve International Cooperation
Zahra Roozbahani *,
Arman Process Journal, -
Predicting customer churn in the fast-Moving consumer goods segment of the retail industry using deep learning
Moien Mahdi, *
Mathematics and Computational Sciences, Summer 2024 -
Entrepreneurship: A comaprisiom between service and manufacturing firms in Iran
*, Faezeh Mohammadipour, Peyman Akhavan, Zeynab Berahmand
Engineering Management and Soft Computing,