title: Global optimization ensemble model for classification methods.
abstract: Supervised learning is the process of data mining for deducing rules from training datasets. A broad array of supervised learning algorithms exists, every one of them with its own advantages and drawbacks. There are some basic issues that affect the accuracy of classifier while solving a supervised learning problem, like bias-variance tradeoff, dimensionality of input space, and noise in the input data space. All these problems affect the accuracy of classifier and are the reason that there is no global optimal method for classification. There is not any generalized improvement method that can increase the accuracy of any classifier while addressing all the problems stated above. This paper proposes a global optimization ensemble model for classification methods (GMC) that can improve the overall accuracy for supervised learning problems. The experimental results on various public datasets showed that the proposed model improved the accuracy of the classification models from 1% to 30% depending upon the algorithm complexity.
authors.affiliation: Department of Computer Engineering, College of Electrical & Mechanical Engineering (E&ME), National University of Sciences and Technology (NUST), H-12, Islamabad 46000, Pakistan.
authors.forename: Hina
authors.initials: H
authors.lastname: Anwar
authors.affiliation: Department of Computer Engineering, College of Electrical & Mechanical Engineering (E&ME), National University of Sciences and Technology (NUST), H-12, Islamabad 46000, Pakistan.
authors.forename: Usman
authors.initials: U
authors.lastname: Qamar
authors.affiliation: Department of Computer Engineering, College of Electrical & Mechanical Engineering (E&ME), National University of Sciences and Technology (NUST), H-12, Islamabad 46000, Pakistan.
authors.forename: Abdul Wahab
authors.initials: AW
authors.lastname: Muzaffar Qureshi
mesh_terms: D000465:Algorithms; D002965:Classification / Q000379:methods*; D057225:Data Mining / Q000379:methods* / Q000592:standards; D019451:Information Management / Q000379:methods* / Q000592:standards; D008962:Models, Theoretical