Meta-learning approach to gene expression data classification

PurposeThe purpose of this paper is to investigate the applicability of meta‐learning to the problem of algorithm recommendation for gene expression data classification.Design/methodology/approachMeta‐learning was used to provide a preference order of machine learning algorithms, based on their expected performances. Two approaches were considered for such: k‐nearest neighbors and support vector machine‐based ranking methods. They were applied to a set of 49 publicly available microarray datasets. The evaluation of the methods followed standard procedures suggested in the meta‐learning literature.FindingsEmpirical evidences show that both ranking methods produce more interesting suggestions for gene expression data classification than the baseline method. Although the rankings are more accurate, a significant difference in the performances of the top classifiers was not observed.Practical implicationsAs the experiments conducted in this paper suggest, the use of meta‐learning approaches can provide an efficient data driven way to select algorithms for gene expression data classification.Originality/valueThis paper reports contributions to the areas of meta‐learning and gene expression data analysis. Regarding the former, it supports the claim that meta‐learning can be suitably applied to problems of a specific domain, expanding its current practice. To the latter, it introduces a cost effective approach to better deal with classification tasks.