TY - JOUR
T1 - Design Ensemble Machine Learning Model for Breast Cancer Diagnosis
AU - Hsieh, Sheau-Ling
AU - Hsieh, Sung-Huai
AU - Cheng, Po-Hsun
AU - Chen, Chi-Huang
AU - Hsu, Kai-Ping
AU - Lee, I-Shun
AU - Wang, Zhenyu
AU - Lai, Fei-Pei
PY - 2012/10
Y1 - 2012/10
N2 - 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.
AB - 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.
KW - Ensemble learning; Neural fuzzy; KNN; Quadratic classifier; Information gain
U2 - 10.1007/s10916-011-9762-6
DO - 10.1007/s10916-011-9762-6
M3 - Article
VL - 36
SP - 2841
EP - 2847
JO - Journal of Medical Systems
JF - Journal of Medical Systems
SN - 0148-5598
IS - 5
ER -