### Abstract

Neural network has been applied in several classification problems such as in medical diagnosis, handwriting recognition, and product inspection, with a good classification performance. The performance of a neural network is characterized by the neural network's structure, transfer function, and learning algorithm. However, a neural network classifier tends to be weak if it uses an inappropriate structure. The neural network's structure depends on the complexity of the relationship between the input and the output. There are no exact rules that can be used to determine the neural network's structure. Therefore, studies in improving neural network classification performance without changing the neural network's structure is a challenging issue. This paper proposes a method to improve neural network classification performance by constructing a linear model based on the Kalman filter as a post processing. The linear model transforms the predicted output of the neural network to a value close to the desired output by using the linear combination of the object features and the predicted output. This simple transformation will reduce the error of neural network and improve classification performance. The Kalman filter iteration is used to estimate the parameters of the linear model. Five datasets from various domains with various characteristics, such as attribute types, the number of attributes, the number of samples, and the number of classes, were used for empirical validation. The validation results show that the linear model based on the Kalman filter can improve the performance of the original neural network.

Original language | English |
---|---|

Pages (from-to) | 112-122 |

Number of pages | 11 |

Journal | Expert Systems with Applications |

Volume | 49 |

DOIs | |

Publication status | Published - 1 May 2016 |

### Fingerprint

### Keywords

- Classification performance
- Kalman filter
- Linear model
- Neural network

### ASJC Scopus subject areas

- Artificial Intelligence
- Computer Science Applications
- Engineering(all)

### Cite this

*Expert Systems with Applications*,

*49*, 112-122. https://doi.org/10.1016/j.eswa.2015.12.012

**A linear model based on Kalman filter for improving neural network classification performance.** / Siswantoro, Joko; Prabuwono, Anton Satria; Abdullah, Azizi; Idrus, Bahari.

Research output: Contribution to journal › Article

*Expert Systems with Applications*, vol. 49, pp. 112-122. https://doi.org/10.1016/j.eswa.2015.12.012

}

TY - JOUR

T1 - A linear model based on Kalman filter for improving neural network classification performance

AU - Siswantoro, Joko

AU - Prabuwono, Anton Satria

AU - Abdullah, Azizi

AU - Idrus, Bahari

PY - 2016/5/1

Y1 - 2016/5/1

N2 - Neural network has been applied in several classification problems such as in medical diagnosis, handwriting recognition, and product inspection, with a good classification performance. The performance of a neural network is characterized by the neural network's structure, transfer function, and learning algorithm. However, a neural network classifier tends to be weak if it uses an inappropriate structure. The neural network's structure depends on the complexity of the relationship between the input and the output. There are no exact rules that can be used to determine the neural network's structure. Therefore, studies in improving neural network classification performance without changing the neural network's structure is a challenging issue. This paper proposes a method to improve neural network classification performance by constructing a linear model based on the Kalman filter as a post processing. The linear model transforms the predicted output of the neural network to a value close to the desired output by using the linear combination of the object features and the predicted output. This simple transformation will reduce the error of neural network and improve classification performance. The Kalman filter iteration is used to estimate the parameters of the linear model. Five datasets from various domains with various characteristics, such as attribute types, the number of attributes, the number of samples, and the number of classes, were used for empirical validation. The validation results show that the linear model based on the Kalman filter can improve the performance of the original neural network.

AB - Neural network has been applied in several classification problems such as in medical diagnosis, handwriting recognition, and product inspection, with a good classification performance. The performance of a neural network is characterized by the neural network's structure, transfer function, and learning algorithm. However, a neural network classifier tends to be weak if it uses an inappropriate structure. The neural network's structure depends on the complexity of the relationship between the input and the output. There are no exact rules that can be used to determine the neural network's structure. Therefore, studies in improving neural network classification performance without changing the neural network's structure is a challenging issue. This paper proposes a method to improve neural network classification performance by constructing a linear model based on the Kalman filter as a post processing. The linear model transforms the predicted output of the neural network to a value close to the desired output by using the linear combination of the object features and the predicted output. This simple transformation will reduce the error of neural network and improve classification performance. The Kalman filter iteration is used to estimate the parameters of the linear model. Five datasets from various domains with various characteristics, such as attribute types, the number of attributes, the number of samples, and the number of classes, were used for empirical validation. The validation results show that the linear model based on the Kalman filter can improve the performance of the original neural network.

KW - Classification performance

KW - Kalman filter

KW - Linear model

KW - Neural network

UR - http://www.scopus.com/inward/record.url?scp=84953329545&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84953329545&partnerID=8YFLogxK

U2 - 10.1016/j.eswa.2015.12.012

DO - 10.1016/j.eswa.2015.12.012

M3 - Article

AN - SCOPUS:84953329545

VL - 49

SP - 112

EP - 122

JO - Expert Systems with Applications

JF - Expert Systems with Applications

SN - 0957-4174

ER -