Here are links to a project summary and video produced by the students:
Based on that work, GC Image plans to soon release a new version of Investigator ML software to support multi-class pattern recognition from chromatographic analyses of complex sample sets. The new version:
- Extends pattern recognition from binary-class problems to multi-class problems.
- Implements additional ML methods.
- Supports additional methods for data normalization, test-set generation, and cross-validation.
- Provides additional performance metrics and new visualization tools.
In addition to the ML methods in the current software — linear discriminant analysis (LDA) and k-nearest neighbors (KNN), the new version supports quadratic discriminant analysis (QDA), regularized discriminant analysis (RDA), and linear regression (LR). All of the ML methods can be applied to multi-class data sets with additional performance metrics and multi-class visualizations. Data sets can be optionally normalized by mean, range, or standard distributions; test sets can be generated randomly; and cross-validation can be performed with either leave-p-out or k-fold regimes.
If you are interested in serving as a beta-tester for the upcoming new version of GC Image Investigator ML, please send email to firstname.lastname@example.org.