Improving pattern classification of DNA microarray data by using PCA and logistic regression
| Autor: | Humberto Sossa, Luis E. Falcon-Morales, Roberto Vega, Marco A. de Luna, Gildardo Sanchez-Ante, Ricardo Ocampo-Vega |
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| Rok vydání: | 2016 |
| Předmět: |
0301 basic medicine
Computer science Sample (statistics) 02 engineering and technology Machine learning computer.software_genre Logistic regression Theoretical Computer Science 03 medical and health sciences Artificial Intelligence 0202 electrical engineering electronic engineering information engineering Selection (genetic algorithm) business.industry Dimensionality reduction Pattern recognition ComputingMethodologies_PATTERNRECOGNITION 030104 developmental biology Principal component analysis Pattern recognition (psychology) Benchmark (computing) 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Artificial intelligence business computer Curse of dimensionality |
| Zdroj: | Intelligent Data Analysis. 20:S53-S67 |
| ISSN: | 1571-4128 1088-467X |
| DOI: | 10.3233/ida-160845 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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