Optimal features subset selection and classification for iris recognition
Journal on Image and Video Processing - Regular
MAkE: Multiobjective algorithm for k-way equipartitioning of a point set
Applied Soft Computing
Expert Systems with Applications: An International Journal
Proceedings of the International Conference and Workshop on Emerging Trends in Technology
Evolutionary Rough Parallel Multi-Objective Optimization Algorithm
Fundamenta Informaticae
Iris recognition using genetic algorithms and asymmetrical SVMs
Machine Graphics & Vision International Journal
Multi-objective optimization with artificial weed colonies
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
Spanning the pareto front of a counter radar detection problem
Proceedings of the 13th annual conference on Genetic and evolutionary computation
MCPR'11 Proceedings of the Third Mexican conference on Pattern recognition
Application of modified NSGA-II algorithm to multi-objective reactive power planning
Applied Soft Computing
Incorporating distance domination in multiobjective evolutionary algorithm
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
An Improved Medical Decision Support System to Identify the Breast Cancer Using Mammogram
Journal of Medical Systems
De Novo Design of Potential RecA Inhibitors Using MultiObjective Optimization
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
Expert Systems with Applications: An International Journal
Computers and Operations Research
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The concept of multiobjective optimization (MOO) has been integrated with variable length chromosomes for the development of a nonparametric genetic classifier which can overcome the problems, like overfitting/overlearning and ignoring smaller classes, as faced by single objective classifiers. The classifier can efficiently approximate any kind of linear and/or nonlinear class boundaries of a data set using an appropriate number of hyperplanes. While designing the classifier the aim is to simultaneously minimize the number of misclassified training points and the number of hyperplanes, and to maximize the product of class wise recognition scores. The concepts of validation set (in addition to training and test sets) and validation functional are introduced in the multiobjective classifier for selecting a solution from a set of nondominated solutions provided by the MOO algorithm. This genetic classifier incorporates elitism and some domain specific constraints in the search process, and is called the CEMOGA-Classifier (constrained elitist multiobjective genetic algorithm based classifier). Two new quantitative indices, namely, the purity and minimal spacing, are developed for evaluating the performance of different MOO techniques. These are used, along with classification accuracy, required number of hyperplanes and the computation time, to compare the CEMOGA-Classifier with other related ones.