Flow Cytometry-Based Classification in Cancer Research: A View on Feature Selection
Hassan, S. Sakira; Ruusuvuori, Pekka; Latonen, Leena; Huttunen, Heikki (2016)
Hassan, S. Sakira
Ruusuvuori, Pekka
Latonen, Leena
Huttunen, Heikki
2016
Julkaisun pysyvä osoite on
https://urn.fi/urn:nbn:fi:tty-201604283888Julkaisun pysyvä osoite on
https://urn.fi/urn:nbn:fi:uta-201609192287
https://urn.fi/urn:nbn:fi:tty-201604283888Julkaisun pysyvä osoite on
https://urn.fi/urn:nbn:fi:uta-201609192287
Kuvaus
Peer reviewed
Tiivistelmä
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 ℓ₁ regularization path of logistic regression classifiers. To this aim, we compare a more traditional cross-validation approach with a recently proposed Bayesian error estimator.
Kokoelmat
- TUNICRIS-julkaisut [16983]