An Advanced Decision Tree-Based Deep Neural Network in Nonlinear Data Classification
Abstract
:1. Introduction
2. Related Work
2.1. Background on Decision Tree
2.2. Neural Networks and Hybrid Models
3. Materials and Methods
3.1. The Proposed DTBDNN Model
3.1.1. System Model Overview
Algorithm 1 Deep Learning Algorithm Forward Propagation Along With Gradient Descent. |
Require: Network depth, L Require: , the weight matrices of the model Require: , the bias parameters of the model Require: X, the input to process Require: y, the target output whiledo while do end while ▹ is the loss function ▹ end while |
3.1.2. Decision Tree-Based Deep Neural Network (DTBDNN) Algorithm
Algorithm 2 Decision Tree-Based Deep Neural Network (DTBDNN) Algorithm. |
|
4. Experimental Results, Performance Evaluations and Discussion
4.1. Dataset and Visualization
4.2. Context-Based Logistic Regression Model’s Result
4.3. The Proposed DTBDNN Model’s Result
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Attributes | Precision | Recall | F1-Score | Support |
---|---|---|---|---|
0 | 0.93 | 0.92 | 0.93 | 500 |
1 | 0.94 | 0.94 | 0.92 | 500 |
Micro Avg | 0.94 | 0.93 | 0.93 | 500 |
Macro Avg | 0.46 | 0.46 | 0.46 | 500 |
Weighted Avg | 0.93 | 0.93 | 0.93 | 500 |
Sample Avg | 0.94 | 0.93 | 0.93 | 500 |
Total | 0.93 | 0.94 | 0.93 | 1000 |
Hidden Layer Size | Accuracy (%) |
---|---|
Accuracy for NN Model (No hidden layers) | 93 |
Accuracy for 1 hidden unit | 71.30 |
Accuracy for 2 hidden units | 70.899 |
Accuracy for 3 hidden units | 70.8 |
Accuracy for 4 hidden units | 93.10 |
Accuracy for 5 hidden units | 92.10 |
Accuracy for 20 hidden units | 93.30 |
Dataset | No. of Instances | No. of Features | No. of Classes | DTBDNN | DT | ELM-Tree |
---|---|---|---|---|---|---|
Wireless Indoor Localization | 2000 | 7 | 4 | 87.21 | 86.79 | 86.4 |
OBS-Network | 1075 | 22 | 4 | 96.76 | 95.87 | 96.12 |
Gime-Me-Some-Credit | 201,669 | 10 | 2 | 97.78 | 91.89 | 95.56 |
SARS B-cell Epitope Prediction | 14,387 | 13 | 2 | 85.34 | 68.93 | 81.1 |
Pima Indian Diabetes | 768 | 8 | 2 | 67.23 | 71.56 | 74.48 |
MAGIC Gamma Telescope | 19,020 | 11 | 2 | 83.56 | 80.76 | 82.58 |
Waveform Noise | 5000 | 40 | 3 | 75.21 | 69.76 | 75.2 |
Credit Approval | 690 | 15 | 2 | 81.35 | 83.32 | 81.23 |
Healthy Older People | 75,128 | 9 | 4 | 97.35 | 95.34 | 96.67 |
Flight Delay | 1,100,000 | 9 | 2 | 77.89 | 66.67 | 75.34 |
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Arifuzzaman, M.; Hasan, M.R.; Toma, T.J.; Hassan, S.B.; Paul, A.K. An Advanced Decision Tree-Based Deep Neural Network in Nonlinear Data Classification. Technologies 2023, 11, 24. https://doi.org/10.3390/technologies11010024
Arifuzzaman M, Hasan MR, Toma TJ, Hassan SB, Paul AK. An Advanced Decision Tree-Based Deep Neural Network in Nonlinear Data Classification. Technologies. 2023; 11(1):24. https://doi.org/10.3390/technologies11010024
Chicago/Turabian StyleArifuzzaman, Mohammad, Md. Rakibul Hasan, Tasnia Jahan Toma, Samia Binta Hassan, and Anup Kumar Paul. 2023. "An Advanced Decision Tree-Based Deep Neural Network in Nonlinear Data Classification" Technologies 11, no. 1: 24. https://doi.org/10.3390/technologies11010024
APA StyleArifuzzaman, M., Hasan, M. R., Toma, T. J., Hassan, S. B., & Paul, A. K. (2023). An Advanced Decision Tree-Based Deep Neural Network in Nonlinear Data Classification. Technologies, 11(1), 24. https://doi.org/10.3390/technologies11010024