# The Additive Input-Doubling Method Based on the SVR with Nonlinear Kernels: Small Data Approach

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## Abstract

**:**

## 1. Introduction

- the design of an SVR-based additive input-doubling method, which provides increase of the prediction accuracy of regression modeling in case of processing short and very short sets of medical data; procedures for its training and application are developed;
- two algorithmic implementations of the developed method are investigated based on the use of two different nonlinear SVR kernels (rbf and polynomial);
- the optimal parameters of the developed algorithms are experimentally determined; the highest prediction accuracy of the proposed algorithms is established compared to other machine learning methods of this class.

## 2. Related Works

- -
- ensemble learning;
- -
- numerical data augmentation.

## 3. Support Vector Machine

## 4. Proposed Method

#### 4.1. Machine Learning in the Case of Short Datasets Using Axial Symmetry of the Response Surface

#### 4.2. SVR-Based Additive Input-Doubling Method

#### 4.2.1. Training Mode

^{2}pairs of vectors ${\overline{x}}_{i}{\overline{x}}_{j}\to {z}_{i,j}^{augm},\text{\hspace{0.17em}}i=1,N;\text{\hspace{0.17em}}j=1,N;\text{\hspace{0.17em}}t=1,{N}^{2}$ (extensions by columns) that will be formed by combining all available vectors of the training dataset, where N is the number of existing observations (extensions by rows).

#### 4.2.2. Application Mode

- the mutual compensation of errors of various signs;
- the principles of ensemble learning by averaging the result.

## 5. Modeling and Results

## 6. Comparison and Discussion

## 7. Conclusions

- the development of input-doubling methods and additive input-doubling methods based on the use of a high-speed RBF-SGTM neural-like structure and its modifications [38]. This will reduce the duration of the training procedure of the developed methods;
- the development of a weighted input-doubling method and additive input-doubling method by replacing expression (15) with a neural network, in particular a non-iterative, corrective SGTM neural-like structure. This will allow the implementation of the procedure for weighing the results (15) instead of the usual summation, which will increase the prediction accuracy;
- the application of clustering and input doubling methods for efficient processing of middle-sized datasets;
- the evaluation of the designed method for the solution of other real tasks in different application areas using a large number of short datasets.

## Author Contributions

## Funding

## Conflicts of Interest

## Appendix A

**Figure A1.**Error values for all methods investigated using the second short dataset: (

**a**) RMSE values; (

**b**) MAE values.

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**Figure 2.**The error values for both training and application modes when changing the number of epochs of the training algorithm. Two algorithmic implementations of the developed method are investigated: (

**a**) RMSE values for the proposed method based on the rbf kernel; (

**b**) RMSE values for the proposed method based on the polynomial kernel; (

**c**) MAE values for the proposed method based on the rbf kernel; (

**d**) MAE values for the proposed method based on the polynomial kernel.

**Figure 5.**Error values for the additive input-doubling method and the input-doubling method in the test mode when changing the number of epochs of the training algorithm. Investigation of two different algorithmic implementations of the studied methods, other things being equal: (

**a**) RMSE values for the investigated methods based on the rbf kernel; (

**b**) MAE values for the investigated methods based on the rbf kernel; (

**c**) RMSE values for the investigated methods based on the polynomial kernel; (

**d**) MAE values for the investigated methods based on the polynomial kernel.

Variable’s Title | MIN Value | MEAN Value | MAX Value |
---|---|---|---|

The pH reading of the urine | 4.76 | 6.042 | 7.94 |

The osmolarity of the urine | 187.00 | 613.61 | 1236.00 |

Indicator of the presence of calcium oxalate crystals | 0.00 | 0.436 | 1.00 |

The urea concentration in millimoles per liter | 10.00 | 264.141 | 620.00 |

The conductivity of the urine | 5.10 | 20.901 | 38.00 |

The specific gravity of the urine | 1.01 | 1.018 | 1.04 |

The calcium concentration in millimoles per liter | 0.17 | 4.161 | 14.34 |

Method | MAE | RMSE | Training Time, Seconds |
---|---|---|---|

Additive SVR(rbf)-based input-doubling method | Training mode | ||

1.524 | 2.219 | 0.624 | |

Test mode | |||

1.965 | 2.707 | - | |

Additive SVR(poly)-based input-doubling method | Training mode | ||

1.744 | 2.296 | 0.178 | |

Test mode | |||

1.977 | 2.823 | - | |

SVR(rbf)-based input-doubling method | Training mode | ||

1.524 | 2.219 | 0.624 | |

Test mode | |||

2.315 | 3.057 | - | |

SVR(poly)-based input-doubling method | Training mode | ||

1.775 | 2.413 | 0.178 | |

Test mode | |||

2.187 | 3.093 | - | |

SVR(poly) | Training mode | ||

2.065 | 2.815 | 0.001 | |

Test mode | |||

2.937 | 3.728 | - | |

SVR(rbf) | Training mode | ||

2.029 | 2.810 | 0.002 | |

Test mode | |||

2.662 | 3.449 | - | |

Adaptive Boosting * | Training mode | ||

0.449 | 0.603 | 0.239 | |

Test mode | |||

2.317 | 3.06 | - | |

Stochastic Gradient Descent ** | Training mode | ||

2.883 | 4.075 | 0.002 | |

Test mode | |||

2.578 | 4.115 | - |

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**MDPI and ACS Style**

Izonin, I.; Tkachenko, R.; Shakhovska, N.; Lotoshynska, N.
The Additive Input-Doubling Method Based on the SVR with Nonlinear Kernels: Small Data Approach. *Symmetry* **2021**, *13*, 612.
https://doi.org/10.3390/sym13040612

**AMA Style**

Izonin I, Tkachenko R, Shakhovska N, Lotoshynska N.
The Additive Input-Doubling Method Based on the SVR with Nonlinear Kernels: Small Data Approach. *Symmetry*. 2021; 13(4):612.
https://doi.org/10.3390/sym13040612

**Chicago/Turabian Style**

Izonin, Ivan, Roman Tkachenko, Nataliya Shakhovska, and Nataliia Lotoshynska.
2021. "The Additive Input-Doubling Method Based on the SVR with Nonlinear Kernels: Small Data Approach" *Symmetry* 13, no. 4: 612.
https://doi.org/10.3390/sym13040612