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— Feature selection refers to the problem of selecting relevant features which produce the most predictive outcome. In particular, feature selection task is involved in datasets containing huge number of features. Rough set theory has been one of the most successful methods used for feature selection. However, this method is still not able to find optimal(More)
Problem statement: Predicting the value for missing attributes is an important data preprocessing problem in data mining and knowledge discovery tasks. Several methods have been proposed to treat missing data and the one used more frequently is deleting instances containing at least one missing value of a feature. When the dataset has minimum number of(More)
Fault localization is an expensive technique in software debugging. Program dependence graphs are used for testing, debugging and maintenance applications in software engineering. Program dependence graphs (PDG) are used to build a probabilistic graphical model of program behavior. In this paper we proposed a model based fault localization technique using(More)
The programmer has to understand the behavior of two similar programs and then identify the execution difference which produces difference in output. When two similar programs are executed under two different environments which shows different behavior in output. The main difference exists in the program behavior is due to two different types of input. This(More)
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