Defect Classification using Relational Association Rule Mining Based on Fuzzy Classifier along with Modified Artificial Bee Colony Algorithm
نویسنده
چکیده
In this study, we introduce a method for defect classification using relational association rule mining based on fuzzy classifier along with modified artificial bee colony algorithm. Relational association rules are an extension of ordinal association rules, which are a particular type of association rules that describe numerical orderings between attributes that commonly occur in the data. These relationships may express quantitative information that may exist in the vector characterizing a software entity. Initially the features are selected from the database in preprocessing phase. The features represent software metrics extracted from the source code. In the database some features are redundant and/or irrelevant which calls for the removal. After that, the rules are generated from the selected feature subset by employing Relational Association Rule Mining (RARM). Then the generated rules are optimized with the help of Modified Artificial Bee Colony (MABC) algorithm. These optimal rules are given as the input for the classifier to identify the defects in the database. The classification can be done by fuzzy classifier. The proposed methodology will be implemented in JAVA by using Promise datasets repository.
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