Abstract
Since many years ago, the scientific community is concerned about how to increase the accuracy of different classification methods, and major achievements have been made so far. Hadoop and MapReduce are used to handle these large volumes of variable size data. This work focuses on the combining a feature selection technique based on genetic algorithm and support vector machines (SVM) of medical disease classification. SVM is relatively a novel classification technique and has been shown higher performance than traditional learning methods in many applications. The idea is to use GA as an optimizer to find the optimal values of hyper-parameters of SVM and adopt a supervised learning approach to train the SVM model. In this paper we propose a genetic algorithm (GA) based classification method.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal Of Engineering And Computer Science
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
