Abstract:
Cancer classification is very important in the field of bioinformatics for diagnosis of cancer
cells. Accurate prediction of cancer is very important for providing better treatment and to
avoid the additional cost associated with wrong therapy. In recent years for classifying the
cancer numbers of methods have been exist. The main objective is to find the smallest set of
genes by using machine learning algorithms. The proposed method initially uses Support
Vector Machine (SVM) classifier with great flexibility. However, the SVM is not suitable for
the classification of large datasets because of significant computational problems. The SVM
combined with the k-means clustering (Km-SVM) is a fast algorithm developed to accelerate
both the training and the prediction of SVM classifiers by using the cluster centers obtained
from the k-means clustering. The new techniques namely weighted K-means-SVM is
implemented to improve accuracy and to reduce misclassification and noise arising from
irrelevant genes. The proposed algorithm was compared with different classifier algorithms
which were applied on the same database. The experimental results showed the superiority of
the proposed algorithm that could achieve a classification accuracy of 85%
