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A New Phylogenetic Inference Based on Genetic Attribute Reduction for Morphological Data

Feng, Jun and Liu, Zeyun and Feng, Hongwei and Sutcliffe, Richard and Liu, Jianni and Han, Jian (2019) 'A New Phylogenetic Inference Based on Genetic Attribute Reduction for Morphological Data.' Entropy, 21 (3). p. 313. ISSN 1099-4300

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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.

Item Type: Article
Uncontrolled Keywords: attribute reduction; information entropy; morphological analysis; phylogenetic tree
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 29 Jul 2019 15:55
Last Modified: 06 Jan 2022 13:59

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