# Multi-test Decision Tree and its Application to Microarray Data Classification

Czajkowski, Marcin, Grzes, Marek, Kretowski, Marek (2014) Multi-test Decision Tree and its Application to Microarray Data Classification. Artificial Intelligence in Medicine, 61 (1). pp. 35-44. ISSN 0933-3657. (doi:10.1016/j.artmed.2014.01.005) (KAR id:48654)

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## Abstract

Objective:

The desirable property of tools used to investigate biological data is

Decision trees are particularly promising in this regard due to their comprehensible nature that resembles the hierarchical process of human decision making. However, existing algorithms for learning decision trees have tendency to underfit gene expression data. The main aim of this work is to improve the performance and stability of decision trees with only a small increase in their complexity.

Methods:

We propose a multi-test decision tree (MTDT); our main contribution is the application of several univariate tests in each non-terminal node of the decision tree. We also search for alternative, lower-ranked features in order to obtain more stable and reliable predictions.

Results:

Experimental validation was performed on several real-life gene expression datasets. Comparison results with eight classifiers show that MTDT has a statistically significantly higher accuracy than popular decision tree classifiers, and it was highly competitive with ensemble learning algorithms. The proposed solution managed to outperform its baseline algorithm on $14$ datasets by an average $6$ percent. A study performed on one of the datasets showed that the discovered genes used in the MTDT classification model

MTDTs are relatively easy to analyze and much more powerful in modeling high dimensional microarray data than their popular counterparts.

Item Type: Article 10.1016/j.artmed.2014.01.005 Decision trees; univariate tests; underfitting; gene expression data Q ScienceQ Science > Q Science (General) > Q335 Artificial intelligence Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing Marek Grzes 26 May 2015 19:57 UTC 16 Feb 2021 13:25 UTC https://kar.kent.ac.uk/id/eprint/48654 (The current URI for this page, for reference purposes) https://orcid.org/0000-0003-4901-1539