Hsieh, S.-L.S.-L.HsiehHsieh, S.-H.S.-H.HsiehCheng, P.-H.P.-H.ChengChen, C.-H.C.-H.ChenHsu, K.-P.K.-P.HsuLee, I.-S.I.-S.LeeWang, Z.Z.WangFEI-PEI LAI2020-04-162020-04-162012https://scholars.lib.ntu.edu.tw/handle/123456789/484412In this paper, we classify the breast cancer of medical diagnostic data. Information gain has been adapted for feature selections. Neural fuzzy (NF), k-nearest neighbor (KNN), quadratic classifier (QC), each single model scheme as well as their associated, ensemble ones have been developed for classifications. In addition, a combined ensemble model with these three schemes has been constructed for further validations. The experimental results indicate that the ensemble learning performs better than individual single ones. Moreover, the combined ensemble model illustrates the highest accuracy of classifications for the breast cancer among all models. ? 2011 Springer Science+Business Media, LLC.Ensemble learning; Information gain; KNN; Neural fuzzy; Quadratic classifier[SDGs]SDG3article; breast cancer; cancer classification; cancer diagnosis; classifier; diagnostic accuracy; ensemble machine learning; fuzzy system; human; k nearest neighbor; machine learning; validation process; artificial neural network; breast tumor; cell adhesion; cell shape; cell size; computer assisted diagnosis; female; fuzzy logic; methodology; radiography; Breast Neoplasms; Cell Adhesion; Cell Shape; Cell Size; Diagnosis, Computer-Assisted; Female; Fuzzy Logic; Humans; Neural Networks (Computer)journal article10.1007/s10916-011-9762-62-s2.0-84867293214https://www.scopus.com/inward/record.uri?eid=2-s2.0-84867293214&doi=10.1007%2fs10916-011-9762-6&partnerID=40&md5=5dfee241fa7dee52f0c30f2b01157197