A Survey of Feature Selection Strategies for DNA Microarray Classification

Classification tasks are difficult and challenging in the bioinformatics field, that used to predict or diagnose patients at an early stage of disease by utilizing DNA microarray technology. However, crucial characteristics technology a large number features small sample sizes, which means confronts "dimensional curse" its classification because high computational execution needed discovery biomarkers difficult. To reduce dimensionality find significant can employ feature selection algorithms not affect performance tasks. Feature helps decrease time removing irrelevant redundant from data. The study aims briefly survey popular methods for classifying technology, such as filters, wrappers, embedded, hybrid approaches. Furthermore, this describes steps process accomplish their relationships other components datasets, cross-validation, classifier algorithms. In case study, we chose four different on two-DNA datasets evaluate discuss performances, namely accuracy, stability, subset size selected features. Keywords: Brief survey; data; selection; filter methods; wrapper embedded methods. DOI: 10.7176/CEIS/14-2-01 Publication date: March 31 st 2023