The global spread of highly pathogenic avian influenza (H5N1) in wild birds and poultry is considered a significant pandemic threat. Furthermore, human infections resulting from direct contact with infected birds/poultry pose a serious public health threat. From November 2003 to March 2007, a total of 3345 H5N1 outbreaks were reported worldwide. Spatial and temporal patterns can provide clues i...

Mutation testing is an effective but operational expensive technique. There exists much improvement to help mutation testing become a wide-used testing technique. The main objective is to reduce the number of mutants and test cases to be executed. The simplified mutant set can still approximate the adequacy. Many new techniques are put forward continuously. One of them is called mutation cluste...

Search-based software testing uses random or directed search techniques to address problems. This paper discusses on test case selection and prioritization by combining genetic and clustering algorithms. Test cases have been generated using genetic algorithm and the prioritization is performed using group-wise clustering algorithm by assigning priorities to the generated test cases thereby redu...

Our previous work shows that traditional randomization partition method for min-max modular (M) classifier can not ensure stable generalization accuracy when the number of two-class problems increases. To overcome this drawback, we consider how to effectively decompose the training data set for a two-class problem in this paper. We propose four basic clustering and anti-clustering strategies an...

We propose a spectral learning approach to shape segmentation. The method is composed of a constrained spectral clustering algorithm that is used to supervise the segmentation of a shape from a training data set, followed by a probabilistic label transfer algorithm that is used to match two shapes and to transfer cluster labels from a training-shape to a test-shape. The novelty resides both in ...

Active learning is an important field of machine learning and it is becoming more widely used in case of problems where labeling the examples in the training data set is expensive. In this paper we present a clustering-based algorithm used in the Active Learning Challenge. The algorithm is based on graph clustering with normalized cuts, and uses kmeans to extract representative points from the ...