Earticle

초록

영어

Many metric-based classification models have been developed and applied to software fault- proneness prediction. This paper presents a novel prediction model using Random Forest classifier. Random Forest (RF) can be a promising candidate for software quality prediction because it is one of the most accurate classification algorithms available and has strengths in noise handling and efficient running on large data sets. The RF model is constructed and the attribute selection process of the input data is performed before the model evaluation. Prediction accuracy of the model is evaluated using two prediction error measures, Type I and Type II error rates, and compared with well-known prediction models, MultiLayer Perceptron (MLP) neural network model and Support Vector Machine (SVM) model. The results show that the RF model significantly outperforms the SVM model and slightly outperforms the MLP model.

목차

Abstract
 1. Introduction
 2. Random Forest Model
 3. Experiment
  3.1 Data Set
  3.2 Attribute Selection
  3.3 Training Results of RF Model
  3.4 Testing Results of RF Model
  3.5 Performance Comparison with Other Models
 4. Conclusion
 Acknowledgements
 References

키워드

저자

• Euyseok Hong [ School of Information Technology, Sungshin Women’s University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Smart Home

• 간기

  격월간

• pISSN

  1975-4094

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605