An Ensemble-Based Feature Selection and Classification of Gene Expression using Support Vector Machine, K-Nearest Neighbor, Decision Tree

Analysis of micro array data for disease prediction is an arduous machine learning job. Due to its high proportions and low samples, the computational risk is high. To increase the efficiency of the classifier a Signal-to-Noise Ratio algorithm can be used to get the most relevant genes. Then to these selected gene datasets, an ensemble-based classification algorithm is applied to get an efficient classifier for future prediction. The ensemble-based algorithm is an optimal combination of a set of learner algorithms. Our research focuses on 3 main classification algorithms namely SVM, KNN and Decision Tree which increases the performance and give confidence to the result. The ensemble is done using majority voting method which outperforms the base classifier.