In this talk, I will present topics from my PhD work. The talk consists of three topics:
Feature selection is a core process in building machine learning models. It is often essential for optimizing performance and sometimes essential to support practical use, such as when the number of features to be measured in the execution stage is limited by hardware or other factors. We examined the procedure in challenging situations: feature selection on a very high-dimensional dataset, feature selection in a privacy-preserving computational environment, and feature selection for a normalization-free classifier.
The second part of the work focuses on stable statistics. Statistically significant results supporting the rejection of the null hypothesis are achieved when the probability of obtaining the test results, or more extreme results, is unlikely to have occurred under the null hypothesis. This probability is the p-value of our observation. Inferred p-values, however, can be very sensitive to inaccuracies in the input data. We introduced and defined the uncertainty that arises from sample labeling errors in the context of statistical tests in a deterministic and stochastic approach. We developed algorithms for efficiently calculating a stability interval around the original p-value. We also developed an algorithm for selecting a differential expression gene set that is robust to label errors.
In the last part of this work, we applied statistical and computational methods in computational biology—virus classifier, aspects of composite DNA synthesis, and a new method for finding gene expression enrichment.
