Title
A New Phylogenetic Inference Based on Genetic Attribute Reduction for Morphological Data.
Abstract
To address the instability of phylogenetic trees in morphological datasets caused by missing values, we present a phylogenetic inference method based on a concept decision tree (CDT) in conjunction with attribute reduction. First, a reliable initial phylogenetic seed tree is created using a few species with relatively complete morphological information by using biologists' prior knowledge or by applying existing tools such as MrBayes. Second, using a top-down data processing approach, we construct concept-sample templates by performing attribute reduction at each node in the initial phylogenetic seed tree. In this way, each node is turned into a decision point with multiple concept-sample templates, providing decision-making functions for grafting. Third, we apply a novel matching algorithm to evaluate the degree of similarity between the species' attributes and their concept-sample templates and to determine the location of the species in the initial phylogenetic seed tree. In this manner, the phylogenetic tree is established step by step. We apply our algorithm to several datasets and compare it with the maximum parsimony, maximum likelihood, and Bayesian inference methods using the two evaluation criteria of accuracy and stability. The experimental results indicate that as the proportion of missing data increases, the accuracy of the CDT method remains at 86.5%, outperforming all other methods and producing a reliable phylogenetic tree.
Year
DOI
Venue
2019
10.3390/e21030313
ENTROPY
Keywords
Field
DocType
attribute reduction,information entropy,morphological analysis,phylogenetic tree
Seed tree,Decision tree,Maximum parsimony,Phylogenetic tree,Bayesian inference,Pattern recognition,Artificial intelligence,Missing data,Statistics,Entropy (information theory),Mathematics,Blossom algorithm
Journal
Volume
Issue
ISSN
21
3
1099-4300
Citations 
PageRank 
References 
0
0.34
0
Authors
6
Name
Order
Citations
PageRank
Jun Feng1147.98
Zeyun Liu200.34
Hongwei Feng312.05
Richard F. E. Sutcliffe431837.67
Jianni Liu500.34
jian han614012.21