<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This paper investigates the multi-layer decision tree optimization algorithm in machine learning and explores its efficiency and accuracy in applying complex data classification. The focus is on the optimization strategy of the algorithm when dealing with large datasets with multiple types of records. The research adopts methods such as data set Discretization, fuzzification processing and hierarchical fuzzy decision tree construction algorithm. Through experimental verification, our constructed multilevel fuzzy decision tree performs well in two application scenarios: rainfall prediction and fingerprint identification. In the rainfall prediction experiment, using 5200 sample data, the algorithm achieves a classification accuracy of 61%. In the fingerprint recognition experiment, from 3,000 fingerprint images, a 100% recognition rate was performed, and the rejection rate was 25.2%. The results show that the multi-layer decision tree optimization algorithm can effectively handle large data sets of multiple types and significantly improve the accuracy and efficiency of classification. The algorithm offers high adaptability and accuracy when dealing with continuous type attributes. This study provides a new perspective and methodology for the application of multi-layer decision trees in machine learning, which is of great significance for the future development of data processing and classification techniques.</p>
</abstract>

This paper investigates the multi-layer decision tree optimization algorithm in machine learning and explores its efficiency and accuracy in applying complex data classification. The focus is on the optimization strategy of the algorithm when dealing with large datasets with multiple types of records. The research adopts methods such as data set Discretization, fuzzification processing and hierarchical fuzzy decision tree construction algorithm. Through experimental verification, our constructed multilevel fuzzy decision tree performs well in two application scenarios: rainfall prediction and fingerprint identification. In the rainfall prediction experiment, using 5200 sample data, the algorithm achieves a classification accuracy of 61%. In the fingerprint recognition experiment, from 3,000 fingerprint images, a 100% recognition rate was performed, and the rejection rate was 25.2%. The results show that the multi-layer decision tree optimization algorithm can effectively handle large data sets of multiple types and significantly improve the accuracy and efficiency of classification. The algorithm offers high adaptability and accuracy when dealing with continuous type attributes. This study provides a new perspective and methodology for the application of multi-layer decision trees in machine learning, which is of great significance for the future development of data processing and classification techniques.

An applied study of multi-layer decision tree optimization algorithms in machine learning

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

This paper investigates the multi-layer decision tree optimization algorithm in machine learning and explores its efficiency and accuracy in applying complex...

An applied study of multi-layer decision tree optimization algorithms in machine learning

Data publikacji: 26 lut 2024

Zakres stron: -

Otrzymano: 17 sty 2024

Przyjęty: 22 sty 2024

DOI: https://doi.org/10.2478/amns-2024-0685

Słowa kluczowe
Multi-layer decision trees, Machine learning, Dataset discretization, Optimization algorithms

© 2024 Jingjing Nie, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

An applied study of multi-layer decision tree optimization algorithms in machine learning

Data publikacji: 26 lut 2024

Zakres stron: -

Otrzymano: 17 sty 2024

Przyjęty: 22 sty 2024

DOI: https://doi.org/10.2478/amns-2024-0685

Słowa kluczoweMulti-layer decision trees, Machine learning, Dataset discretization, Optimization algorithms

© 2024 Jingjing Nie, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Multi-layer decision trees, Machine learning, Dataset discretization, Optimization algorithms