Multi-Labeller classification Method based on Mixture of Classifiers and Genetic Algorithm Optimization

This work presents a new method proposal applied to Multi-Labelers scenarios. This is a situation where labelling individuals in a set of data based on certain characteristics in the process of determining labels to individuals in a set of data based on certain characteristics. Our approach consists in processing a Support Vector Machine classifier to each labelers substantiated on his answers. We formulate a genetic algorithm optimization to obtain a set of weights according to their opinion, in order to penalize each panelist. Finally, their resulting mappings are mixed, and a final classifier is generated, showing to be better than majority vote. For experiments, the well-known Iris database is handled, with multiple simulated artificial labels. The proposed method reaches very good results compared to conventional multilabeler methods, able to assess the concordance among panelists considering the structure data. Abstract— Multi-labeller, multicriteria optimization, genetic algorithm, Gaussian distribution, support vector machine. Multi-labeller, multicriteria optimization, genetic algorithm, Gaussian distribution, support vector machine.