title: Construction of Pancreatic Cancer Classifier Based on SVM Optimized by Improved FOA.
abstract: A novel method is proposed to establish the pancreatic cancer classifier. Firstly, the concept of quantum and fruit fly optimal algorithm (FOA) are introduced, respectively. Then FOA is improved by quantum coding and quantum operation, and a new smell concentration determination function is defined. Finally, the improved FOA is used to optimize the parameters of support vector machine (SVM) and the classifier is established by optimized SVM. In order to verify the effectiveness of the proposed method, SVM and other classification methods have been chosen as the comparing methods. The experimental results show that the proposed method can improve the classifier performance and cost less time.
authors.affiliation: Software College, Northeastern University, Shenyang 110819, China.
authors.forename: Huiyan
authors.initials: H
authors.lastname: Jiang
authors.affiliation: Software College, Northeastern University, Shenyang 110819, China.
authors.forename: Di
authors.initials: D
authors.lastname: Zhao
authors.affiliation: Software College, Northeastern University, Shenyang 110819, China.
authors.forename: Ruiping
authors.initials: R
authors.lastname: Zheng
authors.affiliation: School of Science and Technology, Nottingham Trent University, Nottingham NG17 8NU, UK.
authors.forename: Xiaoqi
authors.initials: X
authors.lastname: Ma
mesh_terms: D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D008962:Models, Theoretical; D010190:Pancreatic Neoplasms / Q000145:classification* / Q000175:diagnosis / Q000453:epidemiology; D010363:Pattern Recognition, Automated; D060388:Support Vector Machine