Comparative Analysis of Machine Learning Algorithms with Optimization Purposes

The field of optimization and machine learning are increasingly interplayed and optimization in different problems leads to the use of machine learning approachesý. ýMachine learning algorithms work in reasonable computational time for specific classes of problems and have important role in extracting knowledge from large amount of dataý. ýIn this paperý, ýa methodology has been employed to optimize the precision of defect detection of concrete slabs depending on their qualitative evaluationý. ýBased on this ideaý, ýsome machine learning algorithms such as C4.5 decision treeý, ýRIPPER rule learning method and Bayesian network have been studied to explore the defect of concrete and to supply a decision system to speed up the defect detection processý. ýThe results from the examinations show that the proposed RIPPER rule learning algorithm in combination with Fourier Transform feature extraction method could get a defect detection rate of 93% as compared to other machine learning algorithms.