In this paper we propose an efficient and effective classifier algorithm for gene expression data analysis. In this research work, the proposed PCA approach is used on improvisation on feature extraction. Once the feature extraction is completed, using Multi Algorithm Fusion (MAF) method is investigated and performed. Also, the Polynomial Support Vector Machine (PSVM) has been related to MAF which supports feature extraction. From this, the absolute weight of SVM, fisher ratio and PSVM are attained. Finally, random forest classifier was utilized for the effective classification approach and it was proved with efficient outcome of measures such as error rate, accuracy, specificity, precision, recall, F1score, false positive rate.