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.

[1]  R. J. Pankhurst,et al.  Practical Taxonomic Computing , 1991 .

[2]  Hong Song,et al.  Interactive Key to Taiwan Grasses Using Characters of Leaf Anatomy - The ActKey Approach , 2005 .

[3]  Q. Peng,et al.  Leaf anatomy of Camellia sect. Oleifera and sect. Paracamellia (Theaceae) with reference to their taxonomic significance , 2007 .

[4]  P. Bryan Heidorn,et al.  A Tool for Multipurpose Use of Online Flora and Fauna: The Biological Information Browsing Environment (BIBE) , 2001, First Monday.

[5]  F. Rindi,et al.  Phylogenetic relationships and species circumscription in Trentepohlia and Printzina (Trentepohliales, Chlorophyta). , 2009, Molecular phylogenetics and evolution.

[6]  M. J. Dallwitz,et al.  A General System for Coding Taxonomic Descriptions , 1980 .

[7]  Changjiang Zhang,et al.  A particle swarm optimization-aided fuzzy cloud classifier applied for plant numerical taxonomy based on attribute similarity , 2009, Expert systems with applications.

[8]  Carl von Linné.  Systema Naturae: Per Regna Tria Naturae, Secundum Classes, Ordines, Genera, Species, Cum Characteribus, Differentiis, Synonymis, Locis, , 2011 .

[9]  James K. Jarvie,et al.  Interactive Keys, Inventory, and Conservation , 1998 .

[10]  Xuemei Shi,et al.  Mining Association Rules with Linguistic Cloud Models , 1998, PAKDD.

[11]  A. Brach,et al.  eFloras: New directions for online floras exemplified by the Flora of China Project , 2006 .

[12]  John McNeill,et al.  International Code of Botanical Nomenclature , 1983 .

[13]  C. G. Cheng,et al.  Comparative leaf anatomy, FTIR discrimination and biogeographical analysis of Camellia section Tuberculata (Theaceae) with a discussion of its taxonomic treatments , 2008, Plant Systematics and Evolution.

[14]  M. J. Dallwitz,et al.  A Comparison of Matrix-Based Taxonomic Identification Systems with Rule-Based Systems , 1992 .

[15]  Hu Hui,et al.  Leaf morphology and anatomy of Camellia section Camellia (Theaceae) , 2009 .

[16]  Anthony R. Brach,et al.  ActKey: a Web-based interactive identification key program , 2005 .

[17]  Xuemei Shi,et al.  Knowledge representation and discovery based on linguistic atoms , 1998, Knowl. Based Syst..

[18]  Ming Dong,et al.  Shortest path based simulated annealing algorithm for dynamic facility layout problem under dynamic business environment , 2009, Expert Syst. Appl..