In healthcare, biomarkers serve an important role in disease classification. Many existing works are focusing in identifying potential biomarkers from gene expression. Moreover, the large number of redundant features in a high dimensional dataset such as gene expression would introduce bias in the classifier and reduce the classifier’s performance. Embedded feature selection methods such as ranked guided iterative feature elimination have been widely adopted owing to the good performance in identification of informative features. However, method like ranked guided iterative feature elimination does not consider the redundancy of the features. Thus, this paper proposes an improved ranked guided iterative feature elimination method by introducing an additional filter selection based on minimum redundancy maximum relevance to filter out redundant features and maintain the relevant feature subset to be ranked and used for classification. Experiments are done using two gene expression datasets for prostate cancer and central nervous system. The performance of the classification is measured in terms of accuracy and compared with existing methods. Meanwhile, biological context verification of the identified features is done through available knowledge databases. Our method shows improved classification accuracy, and the selected genes were found to have relationship with the diseases.
From correlation to causation networks: a simple approximate learning algorithm and its application to high-dimensional plant gene expression data
