Variable Predictive Modeling for Class Discrimination
Classification of data originating from complex process and biological systems is challenging owing to the presence of multivariate and highly nonlinear interactions between variables. Patterns, difficult to distinguish using decision boundaries or available discriminating rules, can be separated based on unique inter-relations among the feature vectors. Given the complex nature of such systems, the variable interaction models are difficult to establish. Genetic programming, a data driven evolutionary modeling approach is suggested here to be a potential tool for designing variable dependency models and exploiting them further for class discriminant analysis. Thus, this paper proposes a new GP-model based classification approach. The approach is applied on illustrative data sets and its performance is benchmarked against well established linear and nonlinear classifiers like LDA, kNN, CART, ANN and SVM. It is demonstrated that GP based models can play an effective role in classification of data into multiple classes.
variable predictive modeling class discrimination genetic programming
K.Raghuraj Rao S.Lakshminarayanan
Informatics and Process Control Unit Department of Chemical and Biomolecular Engineering National University of Singapore,Singapore 117576
国际会议
西安
英文
2007-08-15(万方平台首次上网日期,不代表论文的发表时间)