Lymphoma cancer data classification with eminent features

Microarray techniques are widely employed to monitor the gene expressions that responsible for causing cancer. Freely available microarray lymphoma dataset encompassing 7,129 features with 58 samples in Diffuse Large B-Cell Lymphoma (DLBCL) and 19 Follicular (FL) this research. Feature selection is very crucial classification of cancer high dimension. This paper recommends a structure exploiting two stages feature classify The primary stage performed using correlation dependent technique then K-means clustering applied at secondary gain substantial features. Later those classified as DLBCL FL by means Decision Tree, Artificial Neural Network Elephant Search classifiers. results infer classifier gives higher accuracy 94.95% cases also an 96.23% cases. It concluded together has reached improved comparison other added classifiers used paper.