title: Flow Cytometry-Based Classification in Cancer Research: A View on Feature Selection.
abstract: In this paper, we study the problem of feature selection in cancer-related machine learning tasks. In particular, we study the accuracy and stability of different feature selection approaches within simplistic machine learning pipelines. Earlier studies have shown that for certain cases, the accuracy of detection can easily reach 100% given enough training data. Here, however, we concentrate on simplifying the classification models with and seek for feature selection approaches that are reliable even with extremely small sample sizes. We show that as much as 50% of features can be discarded without compromising the prediction accuracy. Moreover, we study the model selection problem among the ℓ 1 regularization path of logistic regression classifiers. To this aim, we compare a more traditional cross-validation approach with a recently proposed Bayesian error estimator.
authors.affiliation: Department of Signal Processing, Tampere University of Technology, Tampere, Finland.
authors.forename: S Sakira
authors.initials: SS
authors.lastname: Hassan
authors.affiliation: Pori Department, Tampere University of Technology, Pori, Finland.; BioMediTech, University of Tampere, Tampere, Finland.
authors.forename: Pekka
authors.initials: P
authors.lastname: Ruusuvuori
authors.affiliation: BioMediTech, University of Tampere, Tampere, Finland.
authors.forename: Leena
authors.initials: L
authors.lastname: Latonen
authors.affiliation: Department of Signal Processing, Tampere University of Technology, Tampere, Finland.
authors.forename: Heikki
authors.initials: H
authors.lastname: Huttunen
keywords: AML; error estimation; flow cytometry; leukemia; logistic regression; model selection