Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
C4.5: programs for machine learning
C4.5: programs for machine learning
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Towards More Optimal Medical Diagnosing with Evolutionary Algorithms
Journal of Medical Systems
Decision Trees: An Overview and Their Use in Medicine
Journal of Medical Systems
Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Improving Mining of Medical Data by Outliers Prediction
CBMS '05 Proceedings of the 18th IEEE Symposium on Computer-Based Medical Systems
Application of decision trees in problem of air quality modelling in the Czech Republic locality
WSEAS TRANSACTIONS on SYSTEMS
Implementation of classifiers for choosing insurance policy using decision trees: a case study
WSEAS Transactions on Computers
Unified framework for developing testing effort dependent software reliability growth models
WSEAS TRANSACTIONS on SYSTEMS
Three algorithms for analyzing fractal software networks
WSEAS Transactions on Information Science and Applications
Improving the prediction accuracy of liver disorder disease with oversampling
AMERICAN-MATH'12/CEA'12 Proceedings of the 6th WSEAS international conference on Computer Engineering and Applications, and Proceedings of the 2012 American conference on Applied Mathematics
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With the evolution of information technology and software systems, software reliability has become one of the most important topics of software engineering. As the dependency of society on software systems increase, so increases also the importance of efficient software fault prediction. In this paper we present a new approach to improving the classification of faulty software modules. The proposed approach is based on filtering training sets with the introduction of data outliers identification and removal method. The method uses an ensemble of evolutionary induced decision trees to identify the outliers. We argue that a classifier trained by a filtered dataset captures a more general knowledge model and should therefore perform better also on unseen cases. The proposed method is applied on a real-world software reliability analysis dataset and the obtained results are discussed.