Metaheuristic Optimization based Feature Selection for Software Defect Prediction

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Provided by: Academy Publisher
Topic: Software
Format: PDF
Software defect prediction has been an important research topic in the software engineering field, especially to solve the inefficiency and ineffectiveness of existing industrial approach of software testing and reviews. The software defect prediction performance decreases significantly because the data set contains noisy attributes and class imbalance. Feature selection is generally used in machine learning when the learning task involves high-dimensional and noisy attribute datasets. Most of the feature selection algorithms, use local search throughout the entire process, consequently near-optimal to optimal solutions are quiet difficult to be achieved.
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