A Novel Hierarchical Structure for Multilayer Perceptron

Guodong Ma; Zerui Qin

doi:10.47297/taposatWSP2633-456901.20220306

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A Novel Hierarchical Structure for Multilayer Perceptron

Volume 3, Issue 6

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A Novel Hierarchical Structure for Multilayer Perceptron
A Novel Hierarchical Structure for Multilayer Perceptron
1.The Australian University, Canberra 2600,Australia
2.New York University,New York
Vol. 3, Issue 6, Pages: 4-10(2022)
Published： 25 June 2022 ，
DOI：10.47297/taposatWSP2633-456901.20220306
Accepted：

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Ma Guodong,Qin Zerui.A Novel Hierarchical Structure for Multilayer Perceptron[J].Theory and Practice of Science and Technology,2022,03(06):4-10.
DOI：

Ma Guodong,Qin Zerui.A Novel Hierarchical Structure for Multilayer Perceptron[J].Theory and Practice of Science and Technology,2022,03(06):4-10. DOI： 10.47297/taposatWSP2633-456901.20220306.

摘要

Abstract

Based on the training set of the football game FIFA

the project developed a model that could classify the positions of players by their various numerical values. The model can select the best position for a player on the field

providing strong guidance and suggestions for players to improve the game experience. This problem is a multi-classification problem

the most important is to ensure the accuracy of model classification. We first try to use a classification model to classify the whole sample directly

and find that the accuracy is low. Then we introduced "hierarchical classification"

that is to set up a hierarchical classification model and realize the final classification step by step. We choose the neural network model as the classification model by comparing the accuracy of four classification models. In the process of implementation

we also optimized the basic hierarchical classification model innovatively

which greatly improved its performance.

关键词

Keywords

Multilayer PerceptronHierarchical Structure

references

S. Leone, "Fifa 20 complete player dataset," https://www.kaggle.com/ stefanoleone992/fifa-20-complete-player-dataset.

X. wang, "A crawler for player data analysis of fifa football games," https: //cloud.tencent.com/developer/news/368808.

FIFA, "Fifa players," https://sofifa.com/.

J.-P. Alemeida, A. Rutle, and M. Wimmer, "Preface to the 6th international workshop on multi-level modelling (multi 2019)," in 2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C), 2019, pp. 64–65.

francefootball, "francefootball," https://www.francefootball.fr/.

S. Mitrofanov and E. Semenkin, "An approach to training decision trees with the relearning of nodes," in 2021 International Conference on Information Technologies (InfoTech), 2021, pp. 1–5.

L. Abhishek, "Optical character recognition using ensemble of svm, mlp and extra trees classifier," in 2020 International Conference for Emerging Technology (INCET), 2020, pp. 1–4.

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