Misclassification analysis for the class imbalance problem

In classification, the class imbalance issue normally causes the learning algorithm to be dominated by the majority classes and the features of the minority classes are sometimes ignored. This will indirectly affect how human visualise the data. Therefore, special care is needed to take care of the learning algorithm in order to enhance the accuracy for the minority classes. In this study, the use of misclassification analysis is investigated for data re-distribution. Several under-sampling techniques and hybrid techniques using misclassification analysis are proposed in the paper. The benchmark data sets obtained from the University of California Irvine (UCI) machine learning repository are used to investigate the performance of the proposed techniques. The results show that the proposed hybrid technique presents the best performance in the experiment

Long-term balancing selection drives evolution of immunity genes in Capsella.

Genetic drift is expected to remove polymorphism from populations over long periods of time, with the rate of polymorphism loss being accelerated when species experience strong reductions in population size. Adaptive forces that maintain genetic variation in populations, or balancing selection, might counteract this process. To understand the extent to which natural selection can drive the retention of genetic diversity, we document genomic variability after two parallel species-wide bottlenecks in the genus Capsella. We find that ancestral variation preferentially persists at immunity related loci, and that the same collection of alleles has been maintained in different lineages that have been separated for several million years. By reconstructing the evolution of the disease-related locus MLO2b, we find that divergence between ancient haplotypes can be obscured by referenced based re-sequencing methods, and that trans-specific alleles can encode substantially diverged protein sequences. Our data point to long-term balancing selection as an important factor shaping the genetics of immune systems in plants and as the predominant driver of genomic variability after a population bottleneck

Memory-Based Shallow Parsing

We present memory-based learning approaches to shallow parsing and apply these to five tasks: base noun phrase identification, arbitrary base phrase recognition, clause detection, noun phrase parsing and full parsing. We use feature selection techniques and system combination methods for improving the performance of the memory-based learner. Our approach is evaluated on standard data sets and the results are compared with that of other systems. This reveals that our approach works well for base phrase identification while its application towards recognizing embedded structures leaves some room for improvement

Learning Group Formation Factors in a Career and Technical Education Networking Program

Team based learning based on the transformation of permanent student groups into powerful learning teams is widely and successfully used as an instructional strategy in postsecondary career and technical education. Failure of groups to reach the learning team status is a major learning drawback of this approach. Factors affecting the transformation of groups to teams are applied consistently to the whole class, with the exception of group formation and membership. Career and technical education populations differ from other postsecondary populations and examination of group formation factors may result in improvement of student results.Abstract / Introduction / Problem Statement / Purpose of Study / Literature Review / Method / Results / Conclusion / References / Appendix 1 - Consent Form / Appendix 2 Student Questionnaire - Group Selection / Appendix 3 Student Response Dat