Document Details

Document Type : Article In Journal 
Document Title :
A Linear Model Based on Kalman Filter for Improving Neural Network Classification Performance
A Linear Model Based on Kalman Filter for Improving Neural Network Classification Performance
 
Subject : Computer Science 
Document Language : English 
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. 
ISSN : 0957-4174 
Journal Name : Expert Systems With Applications 
Volume : 19 
Issue Number : 2016 
Publishing Year : 1437 AH
2016 AD 
Article Type : Article 
Added Date : Tuesday, March 8, 2016 

Researchers

Researcher Name (Arabic)Researcher Name (English)Researcher TypeDr GradeEmail
Anton Satria PrabuwonoSatria Prabuwono, Anton ResearcherDoctorateantonsatria@eu4m.eu

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