Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Machine Learning - Special issue on learning with probabilistic representations
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Process Modeling, Simulation, and Control for Chical Engineers
Process Modeling, Simulation, and Control for Chical Engineers
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Pattern Recognition Letters
Computational Modeling of Genetic and Biochemical Networks (Computational Molecular Biology)
Computational Modeling of Genetic and Biochemical Networks (Computational Molecular Biology)
Classification by evolutionary ensembles
Pattern Recognition
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Many pattern recognition algorithms applied in literature exhibit data specific performances and are also computationally intense and complex. The data classification problem poses further challenges when different classes cannot be distinguished just based on decision boundaries or conditional discriminating rules. As an alternate to existing methods, inter-relations among the feature vectors can be exploited for distinguishing samples into specific classes. Based on this idea, variable predictive model based class discrimination (VPMCD) method is proposed as a new and alternative classification approach. Analysis is carried out using seven well studied data sets and the performance of VPMCD is benchmarked against well established linear and non-linear classifiers like LDA, kNN, Bayesian networks, CART, ANN and SVM. It is demonstrated that VPMCD is an efficient supervised learning algorithm showing consistent and good performance over these data sets. The new VPMCD method has the potential to be effectively and successfully extended to many pattern recognition applications of recent interest.