title: Design ensemble machine learning model for breast cancer diagnosis.
abstract: In 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.
authors.affiliation: Network and Computer Centre, National Chiao Tung University, Hsinchu, Taiwan.
authors.forename: Sheau-Ling
authors.initials: SL
authors.lastname: Hsieh
authors.forename: Sung-Huai
authors.initials: SH
authors.lastname: Hsieh
authors.forename: Po-Hsun
authors.initials: PH
authors.lastname: Cheng
authors.forename: Chi-Huang
authors.initials: CH
authors.lastname: Chen
authors.forename: Kai-Ping
authors.initials: KP
authors.lastname: Hsu
authors.forename: I-Shun
authors.initials: IS
authors.lastname: Lee
authors.forename: Zhenyu
authors.initials: Z
authors.lastname: Wang
authors.forename: Feipei
authors.initials: F
authors.lastname: Lai
mesh_terms: D001943:Breast Neoplasms / Q000175:diagnosis* / Q000000981:diagnostic imaging; D002448:Cell Adhesion; D048430:Cell Shape; D048429:Cell Size; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D017143:Fuzzy Logic; D006801:Humans; D016571:Neural Networks, Computer; D011859:Radiography