Precise plant classification within genus level based on simulated annealing aided cloud classifier

This is a series research on plant numerical taxonomy, which provides a precise classification method for the description, discrimination, and review of proposals for new or revised plant species to be recognized as taxon units within the genus level. We firstly used all the available quantitative attributes to build cloud models for different sections. Then, the shortest path based simulated annealing algorithm (SPSA) was applied for optimizing these models. After these, the optimized models were validated by the previously used quantitative attribute data. Results showed that cloud models’ accuracy rates of Sect. Tuberculata, Sect. Oleifera and Sect. Paracamellia were 85.00%, 60.00%, 80.00%. And we found some interesting overlaps between the type species and ‘expected species’ that the selected expected species Camellia oleifera and Camellia brevistyla are also type species of Sect. Oleifera and Sect. Paracamellia, respectively. Here we suggest that the expected species be served as an illustration in plant numerical taxonomy. Based on the simulated annealing aided cloud classifier, the taxon hedges, associated with ‘expected species’, were setting to advance our common understanding of sections and improve our capability to recognize and discriminate plant species. These procedures provide a dynamic and practical way to publish new or revised descriptions of species and sections.

Research highlights
► Series application research of improved cloud classifier algorithm.
► Annealing algorithm combined with the cloud classifier.
► Simulated annealing to minimize the edge distances.