Title: Performance identification in large-scale class data from advanced facets of computational intelligence and soft computing techniques
Authors: You-Shyang Chen
Addresses: Department of Information Management, Hwa Hsia University of Technology, 111, Gong Jhuan Road, Chung Ho District, New Taipei City 235, Taiwan
Abstract: Enormous hemodialysis (HD) treatments have caused concern regarding negative information as the world's highest prevalence of end-stage renal disease in Taiwan. This topic is the motivation to identify an adequate HD remedy. Although previous researchers have devised various models to address HD adequacy, the following five deficiencies form obstacles: 1) lack of consideration for imbalanced class problems (ICPs) with medical data; 2) lack of methods to establish mathematical distributions for the given data; 3) lack of explanatory ability on the given data; 4) lack of effective methods to identify the determinants of HD adequacy; 5) lack of appropriate classifiers to define HD adequacy. This study proposes hybrid models to integrate expert knowledge, imbalanced resampling methods, decision tree and random forests-based feature-selection methods, LEM2 algorithm, rough set theory, and rule-filtering techniques to process medical practice with ICPs. These models have better performance than the listed methods from the empirical results.
Keywords: rough set theory; RST; decision tree; random forests; imbalanced class problem data.
International Journal of High Performance Computing and Networking, 2019 Vol.13 No.3, pp.283 - 293
Available online: 13 Mar 2019 *Full-text access for editors Access for subscribers Purchase this article Comment on this article