title: Geometrical bounding of data space and nonlinear classification of chemical data using MPGA algorithm.
abstract: This paper develops a multi-parturition genetic algorithm (MPGA) to be used in geometrical bounding of the overlapped clusters in a data set for the classification of chemical data. Two new operators have been introduced to modify the conventional genetic algorithm, namely, multi-parturition and decimation and orientated creation to improve the linear classification results and diminish the computational time. To circumvent the difficulty commonly encountered in the treatment of linearly inseparable chemical data sets, the optimized linear classifier is further modified to provide a complementary nonlinear classifier. For this reason the space regions of the overlapped clusters have been bounded by erection of half-hyperellipsoids over the linearly misclassified patterns. The proposed MPGA was applied to classify a number of chemical and other data sets with a dimension from 4 to 14. Experimental results have indicated that the proposed MPGA could classify seriously overlapped data sets with an acceptable error rate.
authors.affiliation: State Laboratory for Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, China.
authors.forename: Nasser A M
authors.initials: NA
authors.lastname: Barakat
authors.forename: Zeng-Ping
authors.initials: ZP
authors.lastname: Chen
authors.forename: Jiang-Hui
authors.initials: JH
authors.lastname: Jiang
authors.forename: Ru-Qin
authors.initials: RQ
authors.lastname: Yu