Beschreibung
Breast cancer prediction is an open area of research. Breast cancer is a classification problem which can be solved by machine learning models like a decision tree, random forest, support vector machine, and many more models. Each machine learning model has its own merits and demerits. In breast cancer prediction we need to improve the accuracy of models, so we use here ensemble method which combines predictions of multiple models. An ensemble is a method to increase the prediction accuracy of the breast cancer. In this study, a new technique is introduced to genetic algorithm based weighted average ensemble method of classification data set which overcame the limitations of classical weighted average method. Genetic algorithm based weighted average method is used for the prediction of multiple models. The comparison between Particle swarm optimization (PSO), differential evolution (DE) and Genetic algorithm(GA) and it is concluded that the genetic algorithm outperforms for weighted average methods. One more comparison between classical ensemble method and GA based weighted average method and it is concluded that GA based weighted average methods outperforms.
Autorenporträt
I am extremely glad to lay hand in this important topic of breast cancer. I am beginner in research field. I had started research in machine leaning field. I have done my MTECH from Rajasthan Technical University, Kota, India. By keeping my confidence I poured my entire zeal and enthusiasm to give my best to the ailing society of the women.
Herstellerkennzeichnung:
OmniScriptum SRL
Str. Armeneasca 28/1, office 1
2012 Chisinau
MD
E-Mail: info@omniscriptum.com
