Hybrid-neuro-fuzzy system and adaboost-classifier for classifying breast calcification

One of the major developments in machine learning in the past decade is the Ensemble method, which finds a highly accurate classifier by combining many moderately accurate component classifiers. In this paper, we propose a classifier of integrated neuro-fuzzy system with Adaboost algorithm. It is called Hybrid-neuro-fuzzy system and Adaboost-classifier classifier. Herein, Adaboost creates a collection of component classifiers by maintaining a set of weights over training samples and adaptively adjusting these weights after each iteration, and it is main architecture. The weak learner in Adaboost we used is SONFIN which is a neuro-fuzzy system. And, there is on-line learning ability in SONFIN. Finally, to demonstrate the capability of our proposed classifier, training and testing in different datasets including IRIS datasets, WISCONSIN breast datasets, and CSMU breast datasets are done. The contributions of this paper include implementation of Hybrid-neuro-fuzzy system and Adaboost-classifier for the classification and a classification accuracy of over 98% when training and testing on the IRIS dataset, 99% when training and testing on the WISCONSIN dataset, and 98.8% when training and testing on the CSMU Dataset. This means that our proposed classifier is good for classification and can be applied to variety field in the real world.