Globally, cancer stands as the second leading cause of mortality. Various strategies have been proposed to address this issue, with a strong emphasis on utilizing gene expression data to enhance cancer detection methods. However, challenges arise due to the high dimensionality, limited sample size relative to its dimensions, and the inherent redundancy and noise in many genes. Consequently, it is advisable to employ a subset of genes rather than the entire set for classifying gene expression data. This research introduces a model that incorporates Ranked-based Filter (RF) techniques for extracting significant features and employs Extreme Learning Machine (ELM) for data classification. The computational cost of using RF technique over high dimensional data is low. However extraction of significant genes using one or two stage of reduction is not effective. Thus, a 4-stage feature reduction strategy is applied. The reduced data is then utilized for classification using few variants of ELM model and activation function. Subsequently, a two-stage grading approach is implemented to determine the most suitable classifier for data classification. This analysis is conducted over four microarray gene expression data using four activation function with seven learning based classifiers, from which it is shown that II-ELM classifier outperforms in terms of performance matrix and ROC graph.