Selective voting - Getting more for less in sensor fusion
Many real life problems are characterized by the structure of data derived from multiple sensors. The sensors may be independent, yet their information considers the same entities. Thus, there is a need to efficiently use the information rendered by numerous datasets emanating from different sensors. A novel methodology to deal with such problems is suggested in this work. Measures for evaluating probabilistic classification are used in a new efficient voting approach called "selective voting", which is designed to combine the classification of the models (sensor fusion). Using "selective voting", the number of sensors is decreased significantly while the performance of the integrated model's classification is increased. This method is compared to other methods designed for combining multiple models as well as demonstrated on a real-life problem from the field of human resources.
selective voting decision trees ensemble methods performance measures information fusion machine learning