Improving pattern classification of DNA microarray data by using PCA and logistic regression

Abstract

Abstract. 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.

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Alzheimer’s disease prediction using three machine learning methods

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An Amalgamated Approach to Bilevel Feature Selection Techniques Utilizing Soft Computing Methods for Classifying Colon Cancer

“…In the form of gene expression differences, the formulation and acquisition of data from tissue samples are obtained. Generally, huge size of scientific data brings a lot of problems to the researchers who are trying to identify the useful information for the application of data mining techniques to be used [ 6 ]. This tremendous amount of microarray data is also quite asymmetric in nature, as the number of genes ranges from a few hundreds to many thousands [ 7 ].…”