title: Robust framework to combine diverse classifiers assigning distributed confidence to individual classifiers at class level.
abstract: We have presented a classification framework that combines multiple heterogeneous classifiers in the presence of class label noise. An extension of m-Mediods based modeling is presented that generates model of various classes whilst identifying and filtering noisy training data. This noise free data is further used to learn model for other classifiers such as GMM and SVM. A weight learning method is then introduced to learn weights on each class for different classifiers to construct an ensemble. For this purpose, we applied genetic algorithm to search for an optimal weight vector on which classifier ensemble is expected to give the best accuracy. The proposed approach is evaluated on variety of real life datasets. It is also compared with existing standard ensemble techniques such as Adaboost, Bagging, and Random Subspace Methods. Experimental results show the superiority of proposed ensemble method as compared to its competitors, especially in the presence of class label noise and imbalance classes.
authors.affiliation: Department of Computer Engineering, Bahria University, Islamabad 44000, Pakistan.
authors.forename: Shehzad
authors.identifier: 0000-0003-0899-7354
authors.initials: S
authors.lastname: Khalid
authors.affiliation: Department of Computer Engineering, Bahria University, Islamabad 44000, Pakistan.
authors.forename: Sannia
authors.initials: S
authors.lastname: Arshad
authors.affiliation: Department of Computer Science, COMSATS Institute of Information Technology, Sahiwal, Pakistan ; Department of Computer Science, Bahria University, Islamabad 44000, Pakistan.
authors.forename: Sohail
authors.identifier: 0000-0002-2127-1235
authors.initials: S
authors.lastname: Jabbar
authors.affiliation: Department of Multimedia, Sungkyul University, Anyang-si, Republic of Korea.
authors.forename: Seungmin
authors.initials: S
authors.lastname: Rho
mesh_terms: D001185:Artificial Intelligence / Q000145:classification*; D019451:Information Management / Q000145:classification / Q000379:methods; D013223:Statistics as Topic / Q000379:methods*