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 data sets emanating from different sensors. The proper methodology to deal with such problems is suggested in this work. Measures for evaluating continuous predictions are used in a new efficient voting approach called "selective voting", which is designed to combine the prediction of the models (sensor fusion). Using "selective voting", the number of sensors is decreased significantly while the performance of the integrated model's prediction 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 human resource field.

• Type: Journal
• Year: 2006
• Where: IJPRAI
• Authors: Lior Rokach, Oded Maimon, Reuven Arbel
• Keywords: IJPRAI 2006, Real Life Problem, Selective Voting, Sensors