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Random Forest Parameter Optimization Method Based on Fireworks Algorithm

Published: 24 March 2021 Publication History

Abstract

The construction of a smart city needs to be supported by good machine learning methods, random forest is a kind of technology which has been widely applied in dealing with regression or classification problems. The difficult points to be solved include how to set random forest parameters. The number of subtrees and samples will affect the performance of the algorithm to a great extent. This paper proposes a forest parameter optimization method based on firework algorithm (FWA-RF). By using its explosion, mutation and selection mechanisms, the parameters are adjusted in self-adaptive man-ner to avoid falling into a local optimal state. Through the comparative experiments on 15 UCI databases, the accuracy of the improved algorithm is higher than that of the original RF classification by 1.55% to 26.67%, and is superior to decision tree, Bagging, Adaboost as well as RF based on genetic algorithm. The results indicate that this method can effectively find the range of effective adjustment parameters, make parameter optimization and adjustment easier, and provide technical support for the creation of smart city.

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Cited By

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  • (2025)Advanced filter-based feature selection for improved load identification in solar-powered aircraft distribution systems using XGBoostMeasurement10.1016/j.measurement.2025.116764246(116764)Online publication date: Mar-2025

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cover image ACM Other conferences
EBIMCS '20: Proceedings of the 2020 3rd International Conference on E-Business, Information Management and Computer Science
December 2020
718 pages
ISBN:9781450389099
DOI:10.1145/3453187
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

  • Guilin: Guilin University of Technology, Guilin, China
  • International Engineering and Technology Institute, Hong Kong: International Engineering and Technology Institute, Hong Kong

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 March 2021

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Author Tags

  1. Ensemble learning
  2. Explosion strategy
  3. Firework Algorithm
  4. Mutation strategy
  5. Parameter setting
  6. Random Forest

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  • Refereed limited

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EBIMCS 2020

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EBIMCS '20 Paper Acceptance Rate 112 of 566 submissions, 20%;
Overall Acceptance Rate 143 of 708 submissions, 20%

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View all
  • (2025)Advanced filter-based feature selection for improved load identification in solar-powered aircraft distribution systems using XGBoostMeasurement10.1016/j.measurement.2025.116764246(116764)Online publication date: Mar-2025

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