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The Accurate Detection Of The Malignant Cell Of Breast Cancer, Decrease The Rate Of Mortality Of Women Around The World. The Process Of Feature Optimization, Increase The Probability Of Feature Selection And Mapping Of Data. This Paper Proposed Ensemble-based Classifier For Detection Of Breast Cancer On An Early Stage. The Proposed Ensemble Classifier Support Vector Machine Is A Base Classifier, And The Other Is Boost Classifier. The Firefly Algorithm Reduces The Variance Of Breast Cancer Features For The Selection Of Feature Components For The Classification Algorithm. The Most Dominated Work Is The Extraction Of Features Of Breast Cancer Image. For The Extraction Of Features Applied Wavelet Packet Transform, Wavelet Packet Transform Overcome The Limitation Of Wavelet Transform And Increase The Diversity Of Feature Extraction Process. The Proposed Algorithm Implemented In MATLAB Software And Tested With The Very Reputed Breast Cancer Image Dataset, MIAS And DDSM. The Proposed Algorithm’s Performance Measured With Standard Parameters Such As Accuracy, Specificity, Sensitivity, And MCC. The Evaluated Results Indicate That The Proposed Algorithm Is Better Than DWPT, SVM And BMC. The Increasing Ratio Of The Classification Algorithm Is 8% Instead Of Existing Algorithms.