Search

We present a novel, web-accessible scientific workflow system which makes large-scale comparative studies accessible without programming or excessive configuration requirements. GPFlow allows a workflow defined on single input values to be automatically lifted to operate over collections of input values and supports the formation and processing of collections of values without the need for explicit iteration constructs. We introduce a new model for collection processing based on key aggregation and slicing which guarantees processing integrity and facilitates automatic association of inputs, allowing scientific users to manage the combinatorial explosion of data values inherent in large scale comparative studies. The approach is demonstrated using a core task from comparative genomics, and builds upon our previous work in supporting combined interactive and batch operation, through a lightweight web-based user interface. [ABSTRACT FROM AUTHOR]

We describe a new oblique decision tree induction algorithm. The VQTree algorithm uses Learning Vector Quantization to form a non-parametric model of the training set, and from that obtains a set of hy-perplanes which are used as oblique splits in the nodes of a decision tree. We use a set of public data sets to compare VQTree with two existing decision tree induction algorithms, C5.0 and OC1. Our experiments show that VQTree produces compact decision trees with higher accuracy than either C5.0 or OC1 on some datasets. [ABSTRACT FROM AUTHOR]