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Entropy 2019, 21(3), 313; https://doi.org/10.3390/e21030313

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

1
Department of Information Science and Technology, Northwest University, Xi’an 710127, China
2
Early Life Institute, State Key Laboratory of Continental Dynamics, Department of Geology, Northwest University, Xi’an 710069, China
*
Authors to whom correspondence should be addressed.
Received: 7 February 2019 / Revised: 12 March 2019 / Accepted: 19 March 2019 / Published: 22 March 2019
(This article belongs to the Special Issue Application of Information Theory in Biomedical Data Mining)
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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. View Full-Text
Keywords: attribute reduction; information entropy; morphological analysis; phylogenetic tree attribute reduction; information entropy; morphological analysis; phylogenetic tree
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Feng, J.; Liu, Z.; Feng, H.; Sutcliffe, R.F.E.; Liu, J.; Han, J. A New Phylogenetic Inference Based on Genetic Attribute Reduction for Morphological Data. Entropy 2019, 21, 313.

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