A Novel Weighted Hierarchical Adaptive Voting Ensemble Machine Learning Method for Breast Cancer Detection
Published In
2015 IEEE International Symposium on Multiple-Valued Logic (ISMVL)
Document Type
Citation
Publication Date
5-2015
Subjects
Machine learning -- Application to breast cancer detection, Many-valued logic, Breast -- Cancer -- Diagnosis
Abstract
A novel Weighted Hierarchical Adaptive Voting Ensemble (WHAVE) machine learning (ML) method was developed for breast cancer detection. It was constructed using three individual ML methods based on Multiple-Valued Logic: Disjunctive Normal Form (DNF) rule based method, Decision Trees, Naïve Bays, and one method based on continuous representation: Support Vector Machines (SVM). Results were compared with other methods and show that the WHAVE method accuracy was noticeably higher than the individual ML methods tested. This paper demonstrates that the WHAVE method proposed outperforms all methods researched, and shows the advantage of using WHAVE method for ML in breast cancer detection.
Locate the Document
PSU affiliates use Find in PSU library link at top.
Unaffiliated researchers can access the work here: http://dx.doi.org/10.1109/ISMVL.2015.27
DOI
10.1109/ISMVL.2015.27
Persistent Identifier
http://archives.pdx.edu/ds/psu/16676
Citation Details
Deng, Clemen, and Marek Perkowski. "A Novel Weighted Hierarchical Adaptive Voting Ensemble Machine Learning Method for Breast Cancer Detection." Multiple-Valued Logic (ISMVL), 2015 IEEE International Symposium on. IEEE, 2015
Description
This paper originally appeared in: Multiple-Valued Logic (ISMVL), 2015 IEEE International Symposium on, Issue Date: 18-20 May 2015, © 2015 IEEE.