UCLA

Classifier combination is an effective and popular method to improve the predictive performance of classification models. It has been employed in various fields, including pattern recognition and biometrics. This thesis proposes a novel classifier combination method based on the uniformness, a statistical measurement of the predicted probabilities of base classifiers. By choosing different measurement functions, three combination schemes are explored. The new method is designed to achieve improved accuracy and efficiency on the classification. It is tested on a real multi-class classification problem of plant species using leaf image features, which proves the advantage and robustness of this combination method.