Hybrid Voting Algorithms Using Selected Models for Categorical Data

Although voting methods are a viable way to improve classification algorithm performance, these have usually been applied to complete training datasets. We propose a new voting methodology which is based on the success of each individual classifier as it is applied to particular classes in a training dataset. We test some specific variations on this theme and have found as much as a 12.8% improvement in effectiveness over current voting algorithms.