Optimized Machine Learning Approach for Software Defect Prediction using K-means with Genetic Algorithms - Manjula, C; Florence, Lilly (International Journal of Computer Sciences and Engineering, 2018)
Software defect prediction is one of the most active research areas in software engineering. Machine learning approaches are good in solving these. A predictive model is constructed by using machine learning approaches and classified them into defective and non-defective modules. Clustering is an unsupervised classification method aims at creating groups of objects, or clusters, in such a way that objects in the same cluster are very similar and objects in different clusters are quite distinct. In this paper we proposed a new hybrid approach of K-means clustering algorithm combined with Genetic Algorithm to get the optimum no of clusters. From the present studies it is shown that the performance of the proposed optimized hybrid algorithm is better than the conventional k-means algorithm without optimization.