Multiclass Classification of Cancer Based on Microarray Data Using Extreme Learning Machine

Microarray data are now often used as an alternative in the classification of cancer classes. The challenges in microarray data classification are the huge number of genes and limited samples, so dimensionality reduction and classification algorithm play important roles in building the model. In this research, ReliefF feature selection is applied to reduce the dimensionality of microarray data, then extreme learning machine (ELM) was applied as a classification method. Two benchmark multiclass microarray dataset, GCM and Sup types-Leukemia, are used in this research to evaluate the proposed method. In order to reduce the bias in the end result, 5-cross validation was applied only on training data to select the best combination of parameter values. Then, to evaluate the performance of the proposed method, the experiment of training and testing is repeated ten times using the randomly split of training and testing data and using only the best combination of parameter values. Then, the proposed method is evaluated in term of accuracy and sensitivity. The proposed method show the improvement in accuracy, compare to the previous research, both on GCM and Subtypes-Leukemia dataset. But, the sensitivity among all classes are still not well averaged. The sensitivity become worse on the class of minority sample.