Class Aware Exemplar Discovery from Microarray Gene Expression Data

Given a dataset, exemplars are subset of data points that can represent a set of data points without significance loss of information. Affinity propagation is an exemplar discovery technique that, unlike k-centres clustering, gives uniform preference to all data points. The data points iteratively exchange real-valued messages, until clusters with their representative exemplar become apparent. In this paper, we propose a Class Aware Exemplar Discovery (CAED) algorithm, which assigns preference value to data points based on their ability to differentiate samples of one class from others. To aid this, CAED performs class wise ranking of data points, assigning preference value to each data point based on its class wise rank. While exchanging messages, data points with better representative ability are more favored for being chosen as exemplar over other data points. The proposed method is evaluated over 18 gene expression datasets to check its efficacy for selection of relevant exemplars from large datasets. Experimental evaluation exhibits improvement in classification accuracy over affinity propagation and other state-of-art feature selection techniques. Class Aware Exemplar Discovery converges in lesser iterations as compared to affinity propagation thereby dropping the execution time significantly.