Improving pattern classification of DNA microarray data by using PCA and Logistic Regression

Ricardo Ocampo-Vega, Gildardo Sanchez-Ante, Marco A. De Luna, Roberto Vega, Luis E. Falcón-Morales, Humberto Sossa

Research output: Contribution to conferencePaper

3 Scopus citations


© 2016 - IOS Press and the authors. All rights reserved. DNA microarrays is a technology that can be used to diagnose cancer and other diseases. To automate the analysis of such data, pattern recognition and machine learning algorithms can be applied. However, the curse of dimensionality is unavoidable: very few samples to train, and many attributes in each sample. As the predictive accuracy of supervised classifiers decays with irrelevant and redundant features, the necessity of a dimensionality reduction process is essential. The main idea is to retain only the genes that are the most influential in the classification of the disease. In this paper, a new methodology based on Principal Component Analysis and Logistics Regression is proposed. Our method enables the selection of particular genes that are relevant for classification. Experiments were run using eight different classifiers on two benchmark datasets: Leukemia and Lymphoma. The results show that our method not only reduces the number of required attributes, but also increase the classification accuracy in more than 10% in all the cases we tested.
Original languageAmerican English
Number of pages0
StatePublished - 13 Jul 2016
EventIntelligent Data Analysis -
Duration: 1 Jan 2017 → …


ConferenceIntelligent Data Analysis
Period1/01/17 → …


Cite this

Ocampo-Vega, R., Sanchez-Ante, G., De Luna, M. A., Vega, R., Falcón-Morales, L. E., & Sossa, H. (2016). Improving pattern classification of DNA microarray data by using PCA and Logistic Regression. S53-S67. Paper presented at Intelligent Data Analysis, .