ABSTRACT
Mobile communication provides the medium by which maritime safety information, boat location information, weather forecasts and other services, can be disseminated to boats at sea. For a vessel in trouble, the accuracy and update rate of boat's position reporting aids rescue, thereby avoiding the loss of the boat and saving the lives of the fishermen or preventing an environmental disaster. Our work exploits the power of evolutionary algorithm and machine learning to optimize and classify the boats at sea according to their environmental conditions and mobile signal strength which helps in effective decision making in marine sector.
Index Terms
- Poster: Optimized Ensemble Based Data Classification for Marine Mobile Communication
Recommendations
GA-Ensemble: a genetic algorithm for robust ensembles
Many simple and complex methods have been developed to solve the classification problem. Boosting is one of the best known techniques for improving the accuracy of classifiers. However, boosting is prone to overfitting with noisy data and the final ...
A Novel Genetic Algorithm for Subspace Based Subclasssifier Selection
ICNC '09: Proceedings of the 2009 Fifth International Conference on Natural Computation - Volume 04Ensemble learning constitutes one of the most popular directions in machine learning and data mining currently. And in ensemble learning, feature subspace selection and corresponding classifier ensemble for classification becomes the principal topic, in ...
GAB-EPA: a GA based ensemble pruning approach to tackle multiclass imbalanced problems
ACIIDS'13: Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part IProcessing imbalanced data sets has become a challenging issue in machine learning and data mining communities. Although many researches in the literature have focused on two class problems, multiclass problems have attracted a lot of attention ...
Comments