Uncertain objects, where each feature is represented by multiple observations or a given or fitted probability density function, arise in applications such as sensor networks, moving object databases and medical and biological databases. We propose a methodology to classify uncertain objects based on a new probabilistic distance measure between an uncertain object and a group of uncertain objects. This object-to-group probabilistic distance measure is unique in that it accounts separately for the correlations among the features within each class and within each object. We compare the proposed object-to-group classifier to two existing classifiers, namely, the K-Nearest Neighbor classifier on object means (certain-KNN) and the uncertain-naïve Bayes classifier. In addition, we compare the object-to-group classifier to an uncertain K-Nearest Neighbor classifier (uncertain-KNN), also proposed here, that uses existing probabilistic distance measures for object-to-object distances. We illustrate the advantages of the proposed classifiers with both simulated and real data.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Cognitive Neuroscience
- Artificial Intelligence
- Data mining
- Probabilistic distance measures
- Uncertain data