Search

The principal observed progressive swim types of sperm cells are linear mean and circular swim. Using motility characteristic parameters produced by CASA systems, we perform a parameter subset search to produce distinct clusters of the different swim types. For this task, the artificial bee colony algorithm (an iterative search algorithm modeled after the collective behavior of bees) and the well-studied k-means clustering algorithm were used on simulated and human sperm swim data. The result is distinct clusters with features of each types of swim. The clustering approach displays potential as a tool for automated sperm swim subpopulation analysis.

For classifier ensemble systems, diversity is considered as an important factor for good generalization. In this paper, a classifier ensemble algorithm with relevance-based feature subset selection for classification is proposed. Firstly, a combined maximal class relevance and minimal feature relevance criterion is presented to evaluate candidate features, and to search diverse feature subspaces for classifier ensemble. And then bootstrap sampling is implemented on the feature subspaces for diverse training sets. Finally, the classifiers are trained on diverse data subspaces selected by feature subset search method with subsequent bootstrap. The experimental results show that the algorithm achieves high classification accuracies with small feature subspaces, which lead to a compact and effective ensemble system.