Microarray Feature Selection and Dynamic Selection of Classifiers for Early Detection of Insect Bite Hypersensitivity in Horses

Microarrays can be employed to better characterise allergies, as interactions between antibodies and allergens in mammals can be monitored. Once the joint dynamics of these elements in both healthy and diseased animals are understood, a model to predict the likelihood of an individual having allergic reactions can be defined. We investigate the potential use of Dynamic Selection (DS) methods to classify protein microarray data, with a case study of equine insect bite hypersensitivity (IBH) disease. To the best of our knowledge DS has not yet been applied to these data types. Since most microarrays datasets have a low number of samples, we hypothesise that DS models will produce satisfactory results due to their ability to perform better when compared to traditional ensemble techniques for similar data. We focus on three research questions: 1) What is the potential of DS for microarray data classification and how does it compare with existing classical classification methods results? 2) how do DS methods perform for the IBH dataset? and 3) does feature selection improve DS performance for this data? A wrapper using backward elimination and embedded with a regularized extreme learning machine are adopted to identify the more relevant features influencing the onset of the disease. Results from traditional classifiers are compared to 21 different DS methods before and after performing feature selection. Our results indicate that DS methods do not outperform single and static classifiers on this high-dimensional dataset and their performance also does not improved after feature selection.