Developed Ensemble Model Based on Multiple Machine Learning Models

DOI

10.2991/978-94-6463-300-9_69How to use a DOI?

Keywords

Machine Learning; Natural Language Processing; Ensemble Model

Abstract

In recent years, the practical application of machine learning and natural language processing models, such as the random forest model and decision tree model, has become widespread. These models have been successfully employed in various domains, including economic forecasting and sentiment analysis, leading to enhanced convenience in people’s lives and improved work efficiency. However, the current predominant use of individual machine learning models has exhibited diminishing performance, making it challenging to enhance their accuracy in certain cases. The objective of this study is to identify several machine learning models with superior performance and subsequently employ their output results as independent variables. A new model is then selected and trained using these variables to improve accuracy. By utilizing ensemble models, the risk of overfitting is mitigated, and the robustness of the models is increased, enabling effective handling of complex problems. In the conducted experiment, the integration of models resulted in a 0.5% improvement over the original best-performing single model, achieving an overall accuracy of 85.6%. Notably, this enhancement successfully predicted the correct outcomes for 12 challenging data points that were difficult to improve using the original single machine learning models.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Weishan Li
PY  - 2023
DA  - 2023/11/27
TI  - Developed Ensemble Model Based on Multiple Machine Learning Models
BT  - Proceedings of the 2023 International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2023)
PB  - Atlantis Press
SP  - 663
EP  - 669
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-300-9_69
DO  - 10.2991/978-94-6463-300-9_69
ID  - Li2023
ER  -