Fuzzy Rule Based Classifier for Software Quality Data
نویسنده
چکیده
Software quality estimation based on measured attributes from previous similar products is an active field of research. Such estimation models must inevitably handle imprecision and uncertainty and hence soft computing techniques are gaining popularity. This paper presents a Fuzzy rule based classifier for software quality data and its performance is compared with Bayesian classifier. The fuzzy rules have been generated using Fuzzy C-Means clustering. The objectives of this paper are threefold. First, Fuzzy C-Means algorithm is applied to a set of Software Quality data and clusters are generated. The nearest data points to each cluster centroid are used to generate optimum set of fuzzy rules which are refined with the help of train data. These rules are used to label the clusters generated by Fuzzy C-Means algorithm Second, these fuzzy rules are used to classify the test data. Third, naïve Bayes classification method is applied to classify the test data. Confusion matrix is generated and results show that the performance of both the classification methods is comparable.
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