- (Dundar et al., 2009) ⇒ M. Murat Dundar, E. Daniel Hirleman, Arun K. Bhunia, J. Paul Robinson, and Bartek Rajwa. (2009). “Learning with a Non-exhaustive Training Dataset: A Case Study: Detection of Bacteria Cultures Using Optical-scattering Technology.” In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2009). doi:10.1145/1557019.1557055
- Categories and Subject Descriptors: I.5.2 Pattern Recognition: Design Methodology — classifier design and evaluation.
- General Terms: Algorithms
For a training dataset with a nonexhaustive list of classes, i.e. some classes are not yet known and hence are not represented, the resulting learning problem is ill-defined. In this case a sample from a missing class is incorrectly classified to one of the existing classes. For some applications the cost of misclassifying a sample could be negligible. However, the significance of this problem can better be acknowledged when the potentially undesirable consequences of incorrectly classifying a food pathogen as a nonpathogen are considered. Our research is directed towards the real-time detection of food pathogens using optical-scattering technology. Bacterial colonies consisting of the progeny of a single parent cell scatter light at 635 nm to produce unique forward-scatter signatures. These spectral signatures contain descriptive characteristics of bacterial colonies, which can be used to identify Bacteria cultures in real time. One bottleneck that remains to be addressed is the nonexhaustive nature of the training library. It is very difficult if not impractical to collect samples from all possible bacteria colonies and construct a digital library with an exhaustive set of scatter signatures. This study deals with the real-time detection of samples from a missing class and the associated problem of learning with a nonexhaustive training dataset. Our proposed method assumes a common prior for the set of all classes, known and missing. The parameters of the prior are estimated from the samples of the known classes. This prior is then used to generate a large number of samples to simulate the space of missing classes. Finally a Bayesian maximum likelihood classifier is implemented using samples from real as well as simulated classes. Experiments performed with samples collected for 28 bacteria subclasses favor the proposed approach over the state of the art.
The dataset and our implementation of the proposed approach is available on the web via the link: http://www.cs.iupui.edu/∼dundar/kdd2009.htm
|2009 LearningwithaNonExhaustiveTrain||M. Murat Dundar|
E. Daniel Hirleman
Arun K. Bhunia
J. Paul Robinson
|Learning with a Non-exhaustive Training Dataset: A Case Study: Detection of Bacteria Cultures Using Optical-scattering Technology||KDD-2009 Proceedings||10.1145/1557019.1557055||2009|
|Author||M. Murat Dundar +, E. Daniel Hirleman +, Arun K. Bhunia +, J. Paul Robinson + and Bartek Rajwa +|
|journal||Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining +|
|title||Learning with a Non-exhaustive Training Dataset: A Case Study: Detection of Bacteria Cultures Using Optical-scattering Technology +|