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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)

Abstract

Objective: The desirable property of tools used to investigate biological data is easy to understand models and predictive decisions. 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 are supported by biological evidence in the literature. Conclusion: This paper introduces a new type of decision tree which is more suitable for solving biological problems. MTDTs are relatively easy to analyze and much more powerful in modeling high dimensional microarray data than their popular counterparts.

Item Type: Article
DOI/Identification number: 10.1016/j.artmed.2014.01.005
Uncontrolled keywords: Decision trees; univariate tests; underfitting; gene expression data
Subjects: Q Science
Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Faculties > Sciences > School of Computing
Faculties > Sciences > School of Computing > Computational Intelligence Group
Depositing User: Marek Grzes
Date Deposited: 26 May 2015 19:57 UTC
Last Modified: 29 May 2019 14:36 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/48654 (The current URI for this page, for reference purposes)
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