title: Multi-test decision tree and its application to microarray data classification.
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%. 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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Faculty of Computer Science, Bialystok University of Technology, Wiejska 45a, 15-351 Bialystok, Poland. Electronic address: m.czajkowski@pb.edu.pl.
authors.forename: Marcin
authors.initials: M
authors.lastname: Czajkowski
authors.affiliation: School of Computer Science, University of Waterloo, 200 University Avenue West, Waterloo, Ontario N2L 3G1, Canada.
authors.forename: Marek
authors.initials: M
authors.lastname: Grześ
authors.affiliation: Faculty of Computer Science, Bialystok University of Technology, Wiejska 45a, 15-351 Bialystok, Poland.
authors.forename: Marek
authors.initials: M
authors.lastname: Kretowski
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D066264:Datasets as Topic; D003663:Decision Trees; D020869:Gene Expression Profiling; D006801:Humans; D046228:Microarray Analysis
keywords: Decision trees; Gene expression data; Underfitting; Univariate tests