An Efficient Cancer Classification using Extreme Learning Machine

This Biological studies progress through the expansion of the expertise technologies. DNA microarrays turn out to be an effective tool utilized in molecular biology and in medicine. DNA micro arrays can be utilized to determining the alterations in expression levels or to identify single nucleotide polymorphisms. One can examine the expression of various genes in a single reaction in fast and effective manner. Microarrays can be utilized to determine the comparative amount of particular mRNAs in two or more tissue samples for thousands of genes concurrently. As the supremacy of this technique has been identified, various open queries arise about suitable examination of microarray data. For the above impenetrability and to obtain better consequences of the system with accuracy a new learning algorithm called Extreme Learning Machine (ELM) is used. ELM overcomes difficulties such as local minima, inappropriate learning rate and overfitting usually occurred by iterative learning techniques and performs the training rapidly. ELM utilizes the error free ANOVA techniques in the preprocessing stage. This paper represents that ANOVA technique can be utilized to normalize microarray data and afford determination of alterations in gene expression that are corrected for potential perplexing effects. The proposed technique is evaluated with the help of Lymphoma data set. The experimental result represents that proposed technique results in better classification accuracies with lesser training time and implementation complexity compared to conventional techniques.