A Study of Optimal Ratio of Data Partition for Neuro - Fuzzy - Based Software Reliability Prediction 


Vol. 8,  No. 2, pp. 175-180, Apr.  2001
10.3745/KIPSTD.2001.8.2.175


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  Abstract

This paper presents the optimal fraction of validation set to obtain a prediction accuracy of software failure count or failure time in the future by a neuro-fuzzy system. Given a fixed amount of training data, the most popular effective approach to avoiding underfitting and overfitting is early stopping, and hence getting optimal generalization. But there is unresolved practical issues : How many data do you assign to the training and validation set? Rules of thumb abound, the solution is acquired by trial-and-error and we spend long time in this method. For the sake of optimal fraction of validation set, the variant specific fraction for the validation set be provided. It shows that minimal fraction of the validation data set is sufficient to achieve good next-step prediction. This result can be considered as a practical guideline in a prediction of software reliability by neuro-fuzzy system.

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[IEEE Style]

S. U. Lee, "A Study of Optimal Ratio of Data Partition for Neuro - Fuzzy - Based Software Reliability Prediction," The KIPS Transactions:PartD, vol. 8, no. 2, pp. 175-180, 2001. DOI: 10.3745/KIPSTD.2001.8.2.175.

[ACM Style]

Sang Un Lee. 2001. A Study of Optimal Ratio of Data Partition for Neuro - Fuzzy - Based Software Reliability Prediction. The KIPS Transactions:PartD, 8, 2, (2001), 175-180. DOI: 10.3745/KIPSTD.2001.8.2.175.

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