Multiclass Decision Forest—A Novel Pattern Recognition Method for Multiclass Classification in Microarray Data Analysis

Abstract

The wealth of knowledge imbedded in gene expression data from DNA microarrays portends rapid advances in both research and clinic. Turning the prodigious and noisy data into knowledge is a challenge to the field of bioinformatics, and development of classifiers using supervised learning techniques is the primary methodological approach for clinical application using gene expression data. In this paper, we present a novel classification method, multiclass Decision Forest (DF), that is the direct extension of the two-class DF previously developed in our lab. Central to DF is the synergistic combining of multiple heterogenic but comparable decision trees to reach a more accurate and robust classification model. The computationally inexpensive multiclass DF algorithm integrates gene selection and model development, and thus eliminates the bias of gene preselection in crossvalidation. Importantly, the method provides several statistical means for assessment of prediction accuracy, prediction confidence, and diagnostic capability. We demonstrate the method by application to gene expression data for 83 small round blue-cell tumors (SRBCTs) samples belonging to one of four different classes. Based on 500 runs of 10-fold crossvalidation, tumor prediction accuracy was approximately 97%, sensitivity was approximately 95%, diagnostic sensitivity was approximately 91%, and diagnostic accuracy was approximately 99.5%. Among 25 genes selected to distinguish tumor class, 12 have functional information in the literature implicating their involvement in cancer. The four types of SRBCTs samples are also distinguishable in a clustering analysis based on the expression profiles of these 25 genes. The results demonstrated that the multiclass DF is an effective classification method for analysis of gene expression data for the purpose of molecular diagnostics.

Mold2Molecular Descriptors for QSAR

“…Decision Forest (DF) [17][18][19] is a classification algorithm developed in our laboratories. In principle, DF combines several accurate decision tree models that use distinct sets of descriptors.…”

An evolutionary algorithm for constructing a decision forest: Combining the classification of disjoints decision trees

“…Assuming that two partitioning structures Z 1 = {{a 2 , a 4 }; {a 5 , a 3 }} and Z 2 = {{a 2 , a 6 }; {a 1 , a 4 , a 3 }{a 5 }} are given over the feature set A = {a 1 , a 2 , a 3 , a 4 , a 5 , a 6 }. We also assume that in the previous generation, the following attribute order has been used in the created oblivious decision trees: a 4 → a 2 ; a 5 → a 3 ; a 2 → a 6 ; a 1 → a 3 → a 4 ; a 5 .…”

“…Recall that by using the GWC operator (and ignoring the mutation operator), the following subsets may be obtained: 4 . The oblivious tree for {a 6 } will be created from scratch.…”

“…The remaining subsets can be (partially or completely) obtained by removing attributes from the existing oblivious decision trees. The tree for {a 1 , a 3 } can be obtained by removing attribute a 4 from a 1 → a 3 → a 4 . This removal is possible as a 4 is located last.…”

“…We weigh the individual opinions, and combine them to reach a final decision. Improvement of accuracy and stability is observed in experiments and applications, especially in life science (see, for instance, Tong et al 3 ; Hong et al 4 ; Xie et al 5 ). In this paper, we are interested in constructing a decision forest for high-dimensional datasets.…”